International Journal of Computer Information Systems and Industrial Management Applications
Hybrid K-Nearest Neighbors with Ant Colony Optimization for Securing data warehouses against inferences
Data Warehouses (DWs) are among the most powerful technologies for storing and managing large volumes of corporate data, which often include sensitive or confidential information. However, they remain vulnerable to inference attacks and insufficient access control mechanisms. In recent years, Artificial Intelligence (AI) techniques have become key tools for enhancing DW security, particularly for detecting and preventing unauthorized inferences. In this work, we propose a hybrid approach that combines the K-Nearest Neighbors (KNN) classification algorithm with the Ant Colony Optimization (ACO) metaheuristic to strengthen data warehouse security. The objective is to minimize inference risks by optimizing variable selection and improving the accuracy of sensitive data classification. The proposed ACO–KNN model was evaluated using a dataset of 1,000 SQL analytical queries generated by the IBM Db2 Query Manager, representing realistic decision-support workloads. Experimental results show that the hybrid model significantly outperforms traditional KNN and other metaheuristic-based methods in terms of prediction accuracy, convergence speed, and inference prevention capability. This demonstrates the model’s potential for practical integration into Business Intelligence (BI) and OLAP environments, contributing to more secure and reliable analytical decision-making.
Copyright (c) 2026 Fatima Zohra Benazza, Djamila Hamdadou, Ilyes Khennak https://creativecommons.org/licenses/by/4.0
Real-Time Air Quality Monitoring in Industrial Zones Using IoT-Powered AI Systems
Air pollution in industrial zones poses significant risks to human health and the environment, making real-time monitoring essential for effective mitigation. Traditional air quality monitoring methods are deficient mainly in terms of spatial coverage and temporal resolution, making it challenging to trace the dynamic changes in the levels of pollutants. Development and implementation of real-time customized industrial area air quality monitoring using Internet of Things, sensors and artificial intelligence analytics. An IoT-enabled sensor network continuously measures key pollutants: PM2.5 and PM10, NO2, SO2, CO, and VOCs. The data is also processed through AI algorithms and LSTM networks to detect anomalies in pollutant level prediction. The experimental findings were a Root Mean Square Error (RMSE) of 2.2 and an R² of 0.90, better than the regular multivariate linear regression model. The alert mechanism of the system responded within an average of 5 seconds with low false positives below 3%, thus providing reliable real-time monitoring. Users gave a high satisfaction rating (average score of 4.7/5) about the system interface, especially on ease of navigation and clarity of information. These findings suggest a practical approach to monitoring and managing air quality in the industrial environment using IoT and AI technology integration to support improvement in public health and environmental sustainability and ensure minimal deployment challenges and cost-effectiveness.
Copyright (c) 2026 Leena Arya, Shashi Mehrotra, CH. Sabitha, Mandalapu Sivaparvathi, Venkata Rajani Katuri, Ravi Rastogi, Katepalli Srivalli Rani https://creativecommons.org/licenses/by/4.0
Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm
Colour image segmentation via multilevel thresholding is a fundamental yet challenging task in computer vision, primarily due to the exponential growth of candidate thresholds. This paper introduces the Trees Social Relationship Algorithm (TSR), a novel metaheuristic inspired by the cooperative behaviour of trees in a forest. TSR integrates hierarchical parallel sub-populations (sub-jungles), a growth-rate-driven selection mechanism, and three tailored operators (proliferation, seedling-proliferation, and layering) to balance exploration and exploitation. The algorithm optimizes entropy-based (Kapur) and variance-based (Otsu) objectives across colour channels. To ensure reproducibility, detailed pseudocode, parameter settings, and complexity analysis are provided. Extensive experiments on BSDS benchmark images compare TSR against ten established metaheuristics (PSO, ABC, BAT, BFO, BSA, Cuckoo, DE, EFO, FA, WDO). Evaluation metrics include PSNR, SSIM, FSIM, GCE, PRI, and VOI. Results demonstrate that TSR achieves superior segmentation quality, robust convergence behaviour, and faster execution, consistently outperforming competitors. Statistical validation using the Wilcoxon rank-sum test further confirms the significance of the improvements.
Copyright (c) 2026 Soheil Fakheri, Mahmoud Alimoradi, Mohammad Reza Yamaghani https://creativecommons.org/licenses/by/4.0
Analysis of Delay in Internet of Things Enabled Network: A State Space Control Perspective
This paper presents a novel control framework designed to address critical challenges of delay and packet loss in networked control systems using edge-cloud integration. The proposed solution utilizes a hybrid approach combining a predictive state-space proportional (PSSP) controller and an adaptive state-space proportional-integral (ASSPI) controller. The predictive controller uses a Luenberger observer to estimate system states, enabling it to proactively compensate for network-induced delays and ensure real-time stability. The adaptive PI controller, a core component of the framework, employs a machine learning algorithm based on Soft Actor-Critic (SAC) with a Long Short-Term Memory (LSTM) network to predict future system states and dynamically adjust control parameters. This intelligent adaptation is further enhanced by integrating Recursive Least Squares (RLS) and an Unscented Kalman Filter (UKF) with an adaptive pole placement technique, providing robust, real-time parameter identification and accurate state estimation. The combined approach demonstrates superior performance in mitigating the effects of variable network latency and data loss, offering a reliable control strategy for latency-sensitive applications. The results show that this integrated compensator is highly effective, paving the way for more resilient and high-performance edge-cloud control systems.
Copyright (c) 2026 Padmaja Mishra, Ajay Kumar Yadav, Rajesh Kumar Patjoshi, Rakhee Panigrahi https://creativecommons.org/licenses/by/4.0
An Approach for Lung Cancer Detection using SMOTE with Convolutional Network
With a significant rise in lung cancer cases globally, especially among both men and women, effective lung cancer detection techniques are critically important. This paper addresses the urgency by employing deep learning techniques for lung cancer detection. Our study is based on categorizing the images into three distinct classes: benign, malignant, and normal cases. To ensure uniformity, images of varying sizes are standardized. Addressing the challenge of data imbalance, this paper employs the “Synthetic Minority Over-sampling Technique” (SMOTE), and further enhance image quality through Gaussian Blur in the preprocessing phase. Subsequently, a “Convolutional Neural Network” (CNN) model named “ImageTriNet”, compare its performance with transfer learning models. The ImageTriNet model exhibits commendable results, after 13 training epochs, attaining an accuracy of 0.98, precision of 0.99, recall of 0.96, and an F1-score of 0.97. This research contributes to the ongoing efforts in leveraging deep learning techniques for accurate and timely detection of lung cancer, showcasing efficacy of our ImageTriNet model in this critical domain.
Copyright (c) 2026 Sri Rupin Potula, Ramani Selvanambi https://creativecommons.org/licenses/by/4.0
Deep Forgery Defense System for Live ‎Agricultural E-Commerce—A ‎Meta-Learning-Based Dynamic Verification ‎Model for Anchor Voiceprints
With the booming development of agricultural products live e-commerce, the threat of deep forgery technology is ‎becoming more and more prominent. In this paper, a deep forgery defense system for live agricultural products ‎e-commerce is constructed to guarantee the authenticity and security of e-commerce. Based on MFCC features for voice ‎feature extraction, and for the defects of ObamaNet synthesized anchor baseline system with large time overhead, a ‎speech synthesis model for live agricultural products e-commerce based on mouth shape classification is proposed to ‎match voice features with mouth shape. In addition, a semi-supervised voice verification model is proposed based on ‎meta-learning, in which the meta-gradient computed by the model on the training set is used as a guide for gradient ‎optimization on the test set, so that the model learns richer knowledge. The experiments show that there is a big difference ‎between the synthesized speech and the prototype speech in terms of voiceprint features, and some syllables also have ‎significant differences in the interval of 3kHz~4kHz, which indicates that the system in this paper can effectively ‎distinguish the real speech from the forged speech, and enhances the reliability of the system, which plays an important ‎role in the field of live e-commerce of agricultural products.‎
Copyright (c) 2026 Zhimin Liang, Wenwen Liu, Shaocong Xu https://creativecommons.org/licenses/by/4.0/
Dynamic update and adaptive prediction analysis of user profiles based on conditional random fields
With the rapid development of mobile internet, user behavior data has experienced explosive growth, posing significant challenges to traditional static user profiling methods. Traditional methods often overlook the dynamic nature of user behavior, making it difficult to accurately capture changes in user interests. To address this issue, this paper proposes a user profiling method based on the Conditional Random Field (CRF) model, combining dynamic updates and adaptive predictive analysis. This method fully leverages the advantages of CRF in modeling long-range dependencies. By designing flexible adaptive feature templates and optimized training strategies, the user profiling can precisely track the evolution of user interests. Experimental results show that the model significantly outperforms traditional methods in terms of accuracy, precision, recall, and F1 score, particularly among highly active user groups. The method not only significantly improves the timeliness and accuracy of user profiles but also provides more reliable data support for personalized recommendation systems and precision marketing. By adapting to changes in user behavior trends, this method offers more effective support for various applications based on user profiles.
Copyright (c) 2026 Jin Li https://creativecommons.org/licenses/by/4.0/
Research on Personalized Training Model of College Vocal Singing Skills Based on Fuzzy Control Algorithm
This paper first identifies three commonly used personalized learning parameters—“learning objectives, learning styles, and cognitive levels”—based on learners' needs. Subsequently, it analyzes the recommendation of personalized learning paths from two dimensions: learners and learning resources, and constructs a personalized learning path recommendation model for vocal singing techniques in higher education institutions. Combining three strategies—division of labor strategy, parameter adaptive adjustment strategy, and simulated annealing strategy incorporating distance factors—to enhance the algorithm's ability to escape local optima, this study proposes a division-of-labor and fuzzy control-based particle swarm optimization algorithm (LDFSPSO) for model solution. Results indicate that the LDFSPSO algorithm can rapidly identify the global optimum of single-peak functions and demonstrates significant advantages when handling multi-peak functions with multiple local extrema points of similar magnitude. The proposed method achieves greater diversity in the resource recommendation sequences for learners and better recommendation effectiveness. Case analysis results also indicate that the LDFSPSO algorithm is more effective in identifying resources that align with learners' needs, and can recommend resources with higher matching degrees for learners with different characteristics.
Copyright (c) 2026 Ge Zhang https://creativecommons.org/licenses/by/4.0/
Digital Teaching Design and Implementation Strategies for Plant Landscape Courses in Colleges and Universities in Smart Education Environments
As a core course in horticulture-related majors, plant landscape courses play an important role in the cultivation of horticultural professionals. To address issues such as insufficient knowledge point connections in plant landscape course teaching under smart education environments, this paper constructs a plant landscape course knowledge graph and, based on this, builds a plant landscape course resource recommendation model using knowledge graphs and convolutional neural networks. Using a feature extraction module, feature vectors representing course attributes and user attributes are extracted, and higher-order aggregation between vectors is performed from multiple perspectives to achieve more precise plant landscape course resource recommendations for users. A digital teaching practice for plant landscape courses was conducted at a university in Y City, T Province. Among the experimental class students who applied this model for digital teaching, only one student had a teaching satisfaction score below 60, significantly fewer than in the control class. After the experiment, the average score of the experimental class students reached 101.18 points, which was 13.46 points higher than that of the control class, proving that the model proposed in this paper has a positive promotional effect on student learning in plant landscape course teaching.
Copyright (c) 2026 Hui Zhang, Nana Tang https://creativecommons.org/licenses/by/4.0/
AI Innovative Dissemination of Red Culture in Civic and Political Education in Colleges and Universities--A New Model of Enterprise Operation Participation
Based on the social network analysis method, this paper proposes the analysis method of red culture dissemination in Civic Education. NEDC-GTOPSIS node influence evaluation algorithm is designed, combining gray correlation analysis and approximation of ideal solution method to double evaluate the influence of nodes. Taking the interaction data of the red culture parenting topic of a university microblogging platform as a sample, the study explores the dissemination mechanism and optimization path of red Civic and political education under the participation of enterprise operation. It is found that among the top 10 active users, 30% of the nodes are operated by enterprises. The actual spreading influence of nodes in the communication network with enterprise operation participation is generally the highest, and the actual spreading influence of the communication network without enterprise operation participation is generally lower. The set of seed nodes determined by NEDC-GTOPSIS achieved the widest spreading of influence, which is superior to other comparative algorithms, proving the reliability of this paper's algorithm for the measurement of node influence.
Copyright (c) 2026 Yuqian Sun https://creativecommons.org/licenses/by/4.0/
Multivariate Statistical Analysis of Athletes' Physical Fitness Data and Optimization of Sports Teaching and Training Strategies
The development of physical quality is of great significance in enhancing one's physical health, mastering sports skills, and improving sports level. In this study, the main factors affecting athletes' physical fitness were investigated by principal component analysis, a multiple regression model was constructed to realize the prediction of athletes' physical fitness, and the validity of the model was verified by partial least squares. The 17 indicators of thletes' physical fitness test were analyzed by principal component analysis, and the important factors affecting athletes' physical fitness were categorized into physical characteristics, morphological development, physiological unctions and flexibility and endurance. After analyzing the correlation between each factor and verifying that each factor affects athletes' physical fitness, a regression model was constructed to predict athletes' physical fitness. The partial least squares regression equation was used to predict the four quantitative indexes of physical quality, and the scatter plots of the four indexes were basically symmetrically distributed on the diagonal, indicating that the multiple regression model constructed in this paper is able to predict the physical quality of athletes. In order to help athletes achieve higher quality development, this paper adopts the stratified training method to further optimize the training strategy of physical education.
Copyright (c) 2026 Hui Zhang, Chunhua Li https://creativecommons.org/licenses/by/4.0/
Research on the Integration of Automation Technology Teaching Strategy and Intelligent Manufacturing Model Based on Differential Evolutionary Algorithm
This paper combines test paper quality indicators to construct a test paper assembly mathematical model and uses an improved differential evolution algorithm to solve the model. It generates an initial population through uniform search of the question bank and dynamically adjusts the mutation rate and crossover rate based on the fitness values of the population. Building on this, the paper adopts a backward design approach based on the OBE philosophy, focusing on three aspects—curriculum content, instructional organization, and instructional feedback—to construct an educational reform strategy and intelligent manufacturing-oriented teaching model that integrates automation technology, characterized by “two increases, two decreases, and one improvement.” Finally, the model was tested on students from a certain school, leading to the conclusion that among the experimental data on difficulty level score distributions of test papers generated by different algorithms, the algorithm proposed in this paper had the smallest distribution at the difficult level, with a minimum of 7.89, indicating that the paper's test paper generation strategy is more effective. Through empirical research, it was found that after applying the teaching model proposed in this paper, students in the experimental class achieved higher average scores on each question in the test compared to the control class. Additionally, as teaching progressed, students in the experimental class demonstrated a gradually superior trend in their mastery of computational concepts compared to students in the control class.
Copyright (c) 2026 Nan Zhang https://creativecommons.org/licenses/by/4.0/
Economic Effect and Quantitative Modeling of the Integration of Cultural Industry and Tourism Industry under the Conditions of Digital Economy
In the context of the rapid development of digital economy, the deep integration of cultural industry and tourism industry has become an important engine to promote the high-quality development of the economy. This paper constructs an evaluation index system for the development of cultural industry and tourism industry based on the panel data of 31 provincial-level administrative regions in China from 2015 to 2024. The coupling coordination degree model is used to quantify the integration level of the two, combined with the spatial econometric model to analyze their economic effects and influencing factors. During the period of 2015-2024, the coupling coordination degree of China's cultural and tourism integration shows an overall upward trend, but is subject to the epidemic impact showing phased fluctuations. Spatially, the level of cultural and tourism integration shows the clustering characteristic of “East high, West low”. The spatial spillover effect of the integrated development of culture and tourism significantly reduces the income gap between urban and rural areas, and the direct and indirect effects both converge negatively, with coefficients of -0.1863 and -0.0756 respectively.
Copyright (c) 2026 Yajing Tan https://creativecommons.org/licenses/by/4.0/
Mathematical Modeling Study on the Effect of Educational Interaction in the Synergistic Model of Labor Education, Ideological and Political Education in Colleges and Universities
Labor education and ideological and political education of college students is related to personal growth, exploring the interactive effect of labor education and ideological and political education mathematical modeling can help educators to understand students comprehensively, carry out personalized guidance, and guide students to improve their literacy. The article firstly constructs the index system of labor education and ideological and political education, and measures the comprehensive development level of the interactive effect of labor education and ideological and political education of each region of Province A through entropy weight method. The article firstly constructs the indicator system of labor education and ideological and political education, and measures the comprehensive development level of the interaction between labor education and ideological and political education by entropy weighting method in each region of province A. The article constructs the dynamic equation model of labor education and ideological and political education, and establishes the threshold panel model, and carries out the comparative analysis of the dynamic relationship between labor education and ideological and political education in province A by stages and regions. In terms of regions, in the early stage of education, due to the influence of economic level and resource endowment and other factors, the interaction effect is better in the eastern region, and the influence of ideological and political education on labor education is greater, the influence of labor education on ideological and political education is greater in the western region, and labor education and ideological and political education in the central region are inhibiting each other.
The Application of Big Data Analytics Methods in Higher Education Management to the Design of Individualized Student Pathways
The arrival of big data technology brings the possibility of realizing personalized education. This study applies big data analysis methods to students' personalized cultivation path, proposes a neurocognitive diagnosis method that integrates forgetting and knowledge point importance, and on this basis, constructs a personalized resource recommendation model based on cognitive diagnosis, which integrates convolutional neural network technology into the joint probability matrix decomposition model, and digs deeply into the information of personalized learning resources. Experiments show that the FAINCD cognitive diagnostic model in this paper shows better student performance prediction effect on different data sets, and its DOA value is above 0.85, which is significantly higher than that of the comparison method.The F-value of the CUPMF resource recommendation model is improved by 5.89%-11.89%, and the MAE value is decreased by 5.96%-14.84%, and the students' learning performance using this paper is improved by 15.81%, which is higher than that of the other methods. 15.81%, which is 2.56% to 5.29% higher than other methods. Therefore, the proposed model has a better effect of resource recommendation and learning facilitation, which can provide a guarantee for the personalized training of students.
Copyright (c) 2026 Shucheng Li https://creativecommons.org/licenses/by/4.0/
Analysis of the Use and Effectiveness of Artificial Intelligence-Assisted Creation Tools in Digital Art and Design
This paper focuses on the practical application of artificial intelligence-assisted creation tools in digital art design, and establishes a digital art model based on 3D animation special effects production technology. The envelope contour detection method and chunk fusion processing technology are used to optimize the 3D modeling process, and the improved HFRA-SRGAN deep learning model is used to achieve super-resolution reconstruction of digital art images. Through comparative experiments, the proposed method is verified to have significant advantages in peak signal-to-noise ratio, screen resolution, collision performance and reconstruction effect. Based on the results of user emotion annotation for digital art image design, user satisfaction is analyzed. The images reconstructed by this paper's method are more in line with human subjective vision, and the global reconstruction quality and detail reconstruction artistry scores reach 4.82 and 4.38, respectively, which are 23.9% and 29.6% higher than the sub-optimal performance of the control method2. User satisfaction scores at the instinctive, behavioral, and reflective levels all exceeded 4.5.
Copyright (c) 2026 Jing Tang https://creativecommons.org/licenses/by/4.0/
Time series Analysis of Students' Ideological Changes in the Collaborative Education Model of Labor Education and Ideological and Political Education in Colleges and Universities
It is of great significance to analyze the change of students' ideological concepts to test the effectiveness and optimize the collaborative education model of labor education ideological and political education in colleges and universities. This paper constructs an evaluation index system for the ideological and moral quality of students in colleges and universities, and uses the hierarchical analysis method to assign weights to the indexes, and then constructs a success evaluation model based on the with the object meta-topologically tractable model, and uses the ARIMA model to conduct a time-series analysis on this basis, so as to explore the changes in the students' ideological concepts and the trend of the implementation of collaborative educating mode. The study shows that the level of students' ideological and moral quality in H colleges and universities in 2018-2022 is high. The selected optimal model AR(2) for predicting the change of students' ideological concepts in college H passed the inverse root detection and fitting detection, and the prediction effect was better, in which the predicted values of the political ideal belief indexes in the three periods of backward 2023-2025 were 34.62, 34.73, and 34.85, respectively.The prediction results show that the overall level of students' ideological and moral qualities in college H in 2018-2025 shows a a year-by-year growth trend, but the growth rate is expected to slow down due to reaching the bottleneck, so it is necessary to continue to implement and optimize the synergistic parenting model of ideological and political education of labor education.
Copyright (c) 2026 Yue Xu https://creativecommons.org/licenses/by/4.0/
Exploring the Path of Cultivating and Managing Graduate Students' Innovative Ability Based on Decision Tree Modeling
Graduate students' innovation ability is the main driving force to promote academic research and scientific and technological progress, but at present there are still many difficulties and obstacles in the cultivation and management path. In this paper, we firstly collected the relevant information of the students in the School of Information Science and Technology of X University of Science and Technology, and constructed the evaluation index system of graduate students' innovation ability from the three aspects of comprehensive innovation thinking ability, innovation quality characteristics and innovation practice ability. Based on the random forest to predict the innovation ability of graduate students, then quantitatively analyze the characteristic importance of the factors influencing the innovation ability of graduate students under different grades by SHAP model. Then the partial dependency diagram was used to further reveal the relationship existing between the influencing factors and graduate students' innovation ability. The prediction accuracy, precision, recall and F1 value of the model are all above 80%, and the importance of the influencing factors varies with different grades of graduate student innovation ability. Finally, this paper proposes a path for the cultivation and management of graduate students' innovation ability at three levels, namely, innovation of interdisciplinary knowledge system, adoption of interactive teaching methods and enrichment of practical programs for innovation ability cultivation, in order to promote the high-quality development of graduate education.
Copyright (c) 2026 Weiwei Guo https://creativecommons.org/licenses/by/4.0/
Art and Design Classroom Driven by Artificial Intelligence: A Study of Teaching Mode Innovation under the Perspective of Multiple Intelligences Theory
Based on the theory of multiple intelligences, this paper explores the innovative path of teaching mode driven by artificial intelligence technology. Image processing technology is used to optimize the art design system, and the image brightness equalization and fusion effect is improved by grid chunk repair and wavelet noise reduction. The visual expression sensitivity difference method is introduced to realize high-precision super-resolution reconstruction. Simulation experiments are designed, and the average signal-to-noise removal rate of the proposed system is 56.04% higher than that of the traditional system when the detection time is 100s. The image processing technique proposed in this paper provides better quality input features for subsequent classification tasks by optimizing image brightness equalization and feature fusion. The model shows comprehensive advantages on all types of images, and in the subjective evaluation, it scores 4.67 and 4.78 in both global reconstruction quality and detail reconstruction artistry, respectively, which are better than the control model.
Research on the Optimization Model of the Content Delivery of College Students' Civic Education in the Online and Offline Education Environment
In both online and offline educational environments, learning behavior data recorded by platforms can be analyzed to determine students' actual learning status, which is beneficial for optimizing educational models and improving teaching effectiveness. This paper investigates the delivery of ideological and political education content to college students, constructing an XGBoost prediction model optimized using the TPE method. Combined with the K-means algorithm and SHAP explanation method, it achieves prediction of the effectiveness of ideological and political education content delivery and analysis of influencing factors. The accuracy rate, misclassification rate, recall rate, precision, F1 score, and AUC value of the TPE-XGBoost predictive model are 91.74%, 8.96%, 93.75%, 91.22%, 0.9247, and 0.9813, respectively, enabling effective prediction of the effectiveness of ideological and political education content delivery for college students. Additionally, the K-means algorithm clustering identifies four categories of ideological and political education content delivery for college students. Additionally, the SHAP value for the feature “teacher professional competence” was the highest, followed by features such as “content timeliness and relevance” and “willingness to translate knowledge into action,” indicating that indicators such as teacher professional competence, content timeliness and relevance, and willingness to translate knowledge into action influence the effectiveness of ideological and political education content delivery. Based on this, an optimized path for the delivery of ideological and political education content for college students was designed, providing new insights for optimizing the teaching effectiveness of ideological and political education in higher education institutions.
Copyright (c) 2026 Pei Zhang https://creativecommons.org/licenses/by/4.0/
Optimizing Student Development Pathways and Personal Competency Enhancement Strategies in Colleges and Universities Using Cluster Analysis
The development of higher education is inseparable from the cultivation and development planning of students. The current quality of higher education development in universities and the rationality and effectiveness of student development planning directly affect the effective value of higher education in serving society. Through empirical analysis and the application of data-driven methods, this study optimizes students' future development pathways, particularly focusing on the development characteristics of undergraduate students from the 2020-2023 cohorts at a comprehensive higher education institution. The study employs an optimized extended centroid clustering algorithm combined with a Bayesian scoring function to classify the clustering results derived from multivariate data—including student growth pathways, participation in extracurricular activities, and mental health status—into five distinct categories with significant differences. Through research experiments, compared to traditional extended centroid clustering methods, the co-word clustering algorithm developed in this study can more clearly demonstrate the developmental characteristics of student groups. The co-word clustering method utilizes keyword data mining to extract sample characteristics of students' multidimensional development, thereby conducting multi-angle clustering to analyze the basic characteristics of student types and providing recommendations for fostering differentiated developmental capabilities among students.
Artificial Intelligence-Driven Corporate Financial Forecasting and Risk Assessment Modeling
With the continuous development of the global economy, the financial risks faced by enterprises have become more and more complex and diverse. In order to achieve more accurate prediction and assessment of enterprise financial risk, this paper constructs financial risk early warning indicators from the perspectives of solvency indicators, operating ability indicators, etc., standardizes the data using Max-Min standardization method, and reduces the dimensionality of the financial risk early warning indicators with the help of Principal Component Analysis (PCA), which reduces the problem of multiple covariance in the process of model construction. Based on the deep learning method, the combination of self-encoder and convolutional neural network is used to construct the enterprise financial prediction and risk assessment model based on AE-ResNet. Comparing this paper's model with traditional models such as ANN, 1DCNN, 2DCNN, etc., this paper's model performs optimally in evaluation indexes such as accuracy (0.9637), TPR (0.9415), G-Mean(0.9533) and AUC (0.9895), and the model has excellent performance.
Copyright (c) 2026 Gang Chen, Yalin Qin https://creativecommons.org/licenses/by/4.0/
Research on the Prediction of Emergency Response Time for Public Emergencies Based on Long and Short-Term Memory Network Models
Efficient emergency response to public emergencies is key to minimizing social losses. This study proposes an ARIMA-LSTM combined measurement model. By employing an optimal weighted combination prediction method, the model integrates the linear trend analysis capabilities of the ARIMA model with the nonlinear time-series dependency learning advantages of the long short-term memory (LSTM) network. This approach extracts the linear and nonlinear features from public emergency response time prediction data, addressing the issue of low simulation accuracy in standalone ARIMA and LSTM models. The experimental results show that the prediction performance of the BP neural network, single ARIMA model, and LSTM model are all inferior to the ARIMA-LSTM combination model. When applying the model in this paper to predict the emergency response time for public emergencies in multiple regions, the average emergency response time is only 2.3 hours, and the positive rate of suspected events and outbreak events in early warning signals is less than 5%, indicating that the model in this paper has a low false alarm rate and high practicality. This provides a reliable path for improving the efficiency of emergency response departments and reducing social losses.
Copyright (c) 2026 Runhang Zhang https://creativecommons.org/licenses/by/4.0/
Research on the Application of Mathematical Optimization Algorithms in Civil Engineering Courses and Strategies for Improving Teaching Effectiveness
Accurate recommendation of teaching resources broadens the feasible path for realizing students' personalized development. This paper constructs a civil engineering course resource recommendation system based on multi-dimensional data view to improve the level of intelligent recommendation of resources. Quantitative data weights are used to convert the multi-objective optimization problem into the optimal solution of objective function value, which reduces the difficulty of solving. The multi-objective particle swarm optimization algorithm (NEMOPSO) is introduced to enhance the global optimization capability and improve the accuracy of resource recommendation. Research shows that NEMOPSO can maintain population diversity and solution comprehensiveness in the process of optimization solving. The recommendation accuracy of single resource or multiple resource tasks ≧90%, memory occupation space ≦66MB, and running time ≦24 s. After the experiment, the mean values of the experimental class in the four engineering thinking dimensions were improved by 1.3486, 1.2787, 1.1631, and 1.2769, respectively, which were significantly different from the pre-experiment at the 0.01 level.
Copyright (c) 2026 Xiya Tang, Zhaochao Li, Guobin Bu, Yufeng He, Ling Shen, Wan Xie, Youzhen Li https://creativecommons.org/licenses/by/4.0/
Generative Model-Based Personalized Design of Non-Heritage Cultural and Creative Products and The Path of Remodeling Rural Cultural Resources
This paper proposes a non-heritage cultural and creative generation product design effect image method based on StyleGAN. On this basis, the personalized design of non-heritage cultural and creative products is carried out, and the weights of the parameters are determined by adopting the method of interactive selection with the participation of target customers. The problem that traditional interactive genetic algorithm cannot effectively reduce user fatigue is improved by mutating individuals and genetic operation, and it is applied to multi-function product configuration design. The required initial shapes and inference rules are extracted from past products, and functional constraints are imposed on the product while obtaining a solution that meets the product design requirements. The results show that StyleGAN model is more effective for personalized design of non-heritage cultural and creative products, and its comprehensive satisfaction rate reaches 85.36%. When the number of iterations reaches 85, the fitness value of the non-heritage cultural and creative products generation model is gradually stabilized and finally reaches 1.5686, which shows that the model in this paper can obtain a satisfactory solution before reaching the maximum number of hereditary generations, proving that it is more effective in the derivation of non-heritage cultural and creative products. Finally, through the three forms of “extraction and innovation, reorganization and deformation, transplantation and grafting”, the cultural symbols are integrated into the design practice to create a brand of non-heritage cultural and creative products with the characteristics of rural cultural resources.
Copyright (c) 2026 Jingjing Ai https://creativecommons.org/licenses/by/4.0/
Constructing a Digitalized Timbre Feature Library for Suona Music of Intangible Cultural Heritage Using Random Forest Classifier
As an important part of the intangible cultural heritage, the digital development of the suona is an innovative combination of modern technology and intangible culture, which can be regarded as one of the ways to inherit the intangible culture in the new era. This paper recognizes the existence of the diversity of suona in traditional applications, and proposes to improve the suona timbre recognition method of random forest according to the musical characteristics of suona. Comparing My SQL and Oracle databases, My SQL is selected as the main technology of digital timbre feature library for suona music, and the functional modules of digital timbre feature library for suona music are designed to be composed of two systems: portal subsystem and background management subsystem. Analyze the accuracy of the suona timbre recognition model of residual network combined with random forest model. Analyze the experience of using the digital display design of suona music database by combining the sensory, emotional, interactive, thinking, aesthetic and entertainment dimensions of experience design. Experiencers were able to gain some gains from sensory, emotional, thinking, aesthetic, and entertainment dimensions, and the mean score of the experience of the digital display design of non-heritage suona music was 4.30.
Copyright (c) 2026 Wenliang Li https://creativecommons.org/licenses/by/4.0/
The Construction of Inheritance Network of Jiangsu Rural Intangible Cultural Heritage Skills Based on Knowledge Mapping and the Innovative Path of Locality
This study focuses on the construction of Jiangsu rural NRM inheritance network and proposes a knowledge graph-driven systematic solution. At the technical level, the NRM ontology framework (covering 99 types of entities and 199 attributes) is constructed based on the CIDOC CRM model, the RCBC named entity recognition model integrating four modules is designed, and the RoBERTa-Effg-Adv relational extraction model is proposed, which is trained by quintuple extraction with PGD confrontation. The RCBC named entity recognition model is realized on a self-constructed dataset with a F1 value of 83.11%, an improvement of 3.87 percentage points over the optimal baseline woBERT. The RoBERTa-Effg-Adv relational extraction model achieves an F1 value of 85.88%, which is significantly better than the BERT-BiLSTM-ATT model (+1.94%). At the application level, the analysis of 20 cultural heritage digital museums found that the quality of resource integration determines the effectiveness of communication (the PR value of a website with balanced inward/outward connectivity can reach 7, and the impact factor is 13.382). The study suggests that knowledge mapping can effectively structure the temporal, spatial, person and craft associations of ICH, while digital dissemination needs to be adapted to a multi-platform content strategy to achieve localized innovation.
Copyright (c) 2026 Bo Wang https://creativecommons.org/licenses/by/4.0/
Strategies for Optimizing the Fengqiao Experience in the New Era to Promote the Grassroots Governance Model in Hebei Province by Using a Multilevel Game Model
This paper investigates the impact of the Fengqiao Experience on grassroots governance models in Hebei Province through a survey on residents' sense of security in community governance during the new era. A Logistic regression model is employed to examine the relationship between the Fengqiao Experience in the new era and grassroots governance in Hebei Province. Subsequently, a game theory model hypothesis is proposed, a game theory model is established and solved, and the hypothesis is verified to obtain the game equilibrium strategy combination. The results indicate that public security forces have a significant impact, while the significance of other types of variables is generally much smaller than that of public security forces, and they exhibit different directions of influence. Public choices are closely related to changes in the penalties imposed by local government supervisory agencies for evasion of responsibility, but as long as the social welfare benefits of local governments are maximized, multi-stakeholder collaborative governance in cities can enter a stable virtuous cycle. Therefore, strategies such as improving existing incentive and constraint systems for officials and establishing supervision and reward/punishment mechanisms for regulatory agencies will help address officials' avoidance of responsibility and promote their proactive role in governance.
Copyright (c) 2026 Suwei Zhang, Yaqin Qin, Lei Meng, Xiangsu Chen, Ping Ding https://creativecommons.org/licenses/by/4.0/
Research on the Role Mechanism of Social Media on the Construction of Intangible Cultural Identity and Its Data-Driven Modeling
With the development of digital networks, the influence of social media has been growing. This paper constructs a model for exploring intangible cultural heritage (ICH) activities called CAMM. Through data preprocessing and sample training, it classifies cultural activity themes and establishes a cultural activity dataset. It calculates metrics such as degree centrality, triangle count, and eigenvector centrality for each activity in the dataset to measure node connectivity, thereby assessing the multifaceted impact of social media on public perception, attitudes, and behaviors toward ICH. The study shows that the out-degree and in-degree centrality ranges for the three categories of cultural activity themes are between 17.16% and 35.05%. The data reliability and validity tests exceed the benchmark of 0.85 and the AVE value of 0.90, respectively, meeting the requirements. The mechanism model is: -0.690 + 0.675 × product interaction + 0.679 × interpersonal interaction + 0.634 × service interaction + 0.657 × educational value + 0.642 × emotional value + 0.681 × social value.
Research on the Optimization Path of Budget Performance Management and Final Account Control Methods of Universities Based on Regression Analysis Approach
The budget performance management of colleges and universities is still in the primary state, and the utilization efficiency of financial funds and input resources needs to be further improved. The study constructs a budget performance evaluation system for colleges and universities from two dimensions of input and output, takes the input data and output data of teaching funds of colleges and universities of A in 2022-2024 as samples, and analyzes the performance allocation of their teaching and research funds by using the DEA-Malmquist method. After that, a multiple regression model is constructed to judge the specific factors affecting the budget performance management and to propose the specific path to optimize the budget performance management.The performance appropriation of teaching and research funds in college A verifies that the current budget performance management of colleges and universities is not ideal, and there is a big difference in the funding of scientific research projects. And it is judged that the main factors affecting the budget performance management of colleges and universities are talent cultivation and manpower input. In this paper, in order to improve the efficiency of final account control, we start from 3 aspects of management mechanism, management process and evaluation system respectively to improve the effect of budget performance management in colleges and universities.
Copyright (c) 2026 Wei Cao https://creativecommons.org/licenses/by/4.0/
Research on Analyzing the Competitive Situation of College Students' Innovation and Entrepreneurship Market Based on the Algorithm of Multilayer Perceptual Machine
In recent years, along with more and more employment problems, the state has formulated a series of policies to support college students' innovation and entrepreneurship, and all colleges and universities are actively exploring the competitive market dynamics. This paper starts from the current situation of college students' innovation and entrepreneurship to understand its main influencing factors. The traditional TF-IDF algorithm is then used to extract text features, and it is found that there are defects in the algorithm, for which the importance value is introduced on the basis of the original algorithm. The extracted text feature vector of college students' innovation and entrepreneurship market competition situation is used as the input layer of the multilayer perceptron algorithm, and the output layer is the prediction value of the market competition situation, and the BP neural algorithm is used for training, which finally completes the task of constructing the prediction model of the college students' innovation and entrepreneurship market competition situation, and empirically analyzes the model. In terms of AUC area, the performance of BP algorithm (index difference: 0.1052) is better than that of random forest algorithm (index difference: 0.1194), which proves that this paper's algorithm is prioritized in the analysis of competitive situation of college students' innovative entrepreneurship market. In addition, this paper's algorithm detects that the number of employment in the tertiary industry increases by 1,479,600 people every year, and the corresponding number of employment in the primary industry and the secondary industry shows a decreasing trend, which quantitatively reflects the current competitive situation in the market of college students' innovation and entrepreneurship.
Copyright (c) 2026 Juan Chen, Xiaofei Li https://creativecommons.org/licenses/by/4.0/
Intelligent Algorithm-Based Multiple Logic Transmission Paths for Common Wealth in Ideological and Political Courses in Colleges and Universities
This study utilizes panel data from 30 provinces (regions and municipalities) spanning the years 2015–2024 and employs dynamic QCA methods to identify the dissemination pathways for promoting common prosperity in higher education ideological and political courses. To validate the reliability of this dissemination pathway, a parameter inversion method based on neural networks was designed. First, a modified SIR epidemic model was selected to construct a common prosperity dissemination model. Then, MATLAB R2014 was used to perform parameter inversion for the dissemination model. Finally, model fitting and trend prediction analysis were conducted for the dissemination of common prosperity. Taking institutional development data as an example, the more dispersed the distribution of institutional development values, the fewer citizens holding extreme opinions during the early stages of common prosperity, and the more stable the dissemination of common prosperity. Validating the extracted real-world data, the model established in this study aligns with the actual trend changes, providing valuable insights for researching the dissemination trends of common prosperity.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Research on Data Fusion and Big Data Analysis Methods in the Optimization of College Students' Innovation and Entrepreneurship Education Curriculum System
Knowledge mapping provides an effective solution to the problem of how to effectively and automatically integrate multimodal curriculum resources, as well as to better organize and present knowledge. In this paper, for the problem of optimizing the curriculum system of innovation and entrepreneurship education for college students, a named entity recognition model based on CNN+BiLSTM-CRF and an entity alignment model with fine-tuned BERT word embeddings are proposed to integrate the multimodal curriculum knowledge and visualize and analyze it. Experiments show that the Precision, Recall, and F1 values of the CNN+BiLSTM-CRF model in this paper reach 96.90%, 96.49%, and 96.69%, respectively, which are all better than those of other comparative models, which fully demonstrates that the proposed model in this paper has a considerable generalization ability. Meanwhile, the entity alignment model BERT+softmax proposed in this paper outperforms other models on each dataset, indicating that its design is more reasonable and can better serve the entity alignment task. In addition, the hot content of innovation and entrepreneurship curriculum research is innovation and entrepreneurship education, entrepreneurial ability and innovation, which can be the main direction of curriculum system optimization.
Copyright (c) 2026 Yadong Chai https://creativecommons.org/licenses/by/4.0/
Research on the Dynamic Prediction Model of Student Behavior in the Information Management System of Ideological and Political Education for Student Groups in Colleges and Universities
This study proposes a dynamic prediction model of student behavior based on multilayer fragment dynamic semantic spatio-temporal graph convolutional network (MF-STGCN) to meet the needs of information management of ideological and political education in colleges and universities. By integrating heterogeneous data from multiple sources, such as campus card consumption, WiFi track, course schedule, etc., a three-level data system of “activity-behavior-abnormal behavior” is constructed. First, activities are defined and clustered into five types of functional areas (teaching, dormitory, cafeteria, etc.). Secondly, we extracted the spatial and temporal characteristics, calculated the proportion of students' stay time in each area on a weekly basis, and formed the spatial and temporal vectors to characterize behavioral preferences. Finally, the MF-STGCN model is designed, which innovatively introduces a multi-fragment segmentation mechanism, combining dynamic semantic graph convolution, temporal convolution and fully connected layers to realize multi-scale feature fusion. The empirical study shows that the entropy value of WiFi usage in the teaching area is the lowest (0.18-0.41), reflecting the class schedule constraints. The dormitory has medium entropy value (0.47-0.72), which rises to 0.68 at the end of the period (schedule disruption). The cafeteria has the highest entropy value (0.65-0.82), which is consistent with fragmentation. MF-STGCN has an average relative error of only 4.23%-5.65% at the size of 100-10,000 people, which is 2.93% lower than K-nearest neighbor and GCN -4.53%, and the operational efficiency is improved by 5-9 times, with 10,000-person prediction taking 35.53 seconds. The model effectively improves the accuracy and timeliness of behavioral prediction, and promotes the transformation of civic education management from empirical decision-making to data-driven.
Copyright (c) 2026 Danqiong Wang https://creativecommons.org/licenses/by/4.0/
Research on Optimal Allocation of Civic Education Resources in Civil Engineering Disciplines Based on Mathematical Planning Methods
In order to better cultivate talents in the new era, it is necessary to strengthen the resource allocation of higher education's Civic and Political Education, so that educators and educated people subconsciously strengthen the cognition of the importance of Civic and Political Education. In this paper, the DEA-BCC model is selected to statically analyze the resource allocation efficiency of higher Civic and Political Education through the static perspective, using the data envelopment analysis method. Combined with Malmquist index and Dagum index, it evaluates the temporal and spatial differences in the allocation efficiency of Civic and Political education resources from a dynamic perspective. Civic and political courses of higher civil engineering majors are selected as assessment objects to construct civil engineering civic and political education resources and evaluate the effectiveness of civic and political resource allocation for civil engineering major courses. Among the 31 provinces in China, the comprehensive efficiency scores of Tianjin, Inner Mongolia, Jilin, Heilongjiang, Zhejiang, Fujian, Hubei, Hainan, and Tibet are 0.914, 0.988, 0.903, 0.845, 0.984, 0.948, 0.936, 0.826, and 0.965, respectively, which are lower than 1, indicating that there is some degree of resource allocation degree of inefficiency in resource allocation. Taking 2012 as the base period, then the efficiency gap in China's higher education resource allocation will decrease by 3.671% per year in 2023, and the spatial gap in China's higher education investment efficiency is shrinking.
Copyright (c) 2026 Zhaochao Li, Xiya Tang, Guobin Bu, Ling Shen, Yufeng He, Wan Xie, Youzhen Li https://creativecommons.org/licenses/by/4.0/
Design and Practice of Optimization Algorithm for College Career Planning Service Based on Cloud Computing Platform
Career planning is an important part of higher education, and effective career planning can not only help college students clarify their career goals, but also provide them with paths to realize their career goals. The article designs a cloud computing-based career planning recommendation system for college students, adopting the three-layer architecture of cloud computing, combining the student information model, the career information model and the collaborative filtering recommendation algorithm with improved CVAE and CPMF to realize the accurate matching of student and career information. In this paper, an empirical study is carried out with students from five colleges and universities. In this study, the gender difference of college students as a group has a certain impact on career, and generally speaking, the level of career planning of male students is higher than that of female students. The part-time experience is also reflected in the internship experience or not, and this aspect is the main influence on the level of career planning. The practical results show that it is feasible to carry out the career planning service for colleges and universities based on cloud computing platform.
An Analysis of the Application of Mathematical and Statistical Models in the Optimization of Educational Resource Allocation in Colleges and Universities
This paper focuses on the practical application of mathematical statistical models for the allocation of educational resources in higher education institutions under the premise of resource optimization. It takes factors such as the level of resource investment, resource utilization efficiency, and the degree of research platform development as entry points to explore their impact on the efficiency of educational resource allocation. Using Data Envelopment Analysis (DEA) models, Structural Equation Modeling (SEM) methods, and factor analysis, this study analyzes the allocation of educational resources in selected Chinese higher education institutions. Based on a survey of educational resources in 256 Chinese higher education institutions, it was found that the efficiency of educational resource allocation in Chinese universities is generally low, with only 9.38% of institutions achieving effective resource allocation. The study also concluded that the level of regional economic development significantly influences the efficiency of resource allocation in higher education institutions, with universities in eastern regions generally achieving higher resource allocation efficiency than those in central and western regions. Technological progress remains the primary factor driving improvements in resource allocation efficiency, with its contribution rate to resource allocation efficiency reaching as high as 68.4%. Therefore, this paper suggests establishing a data-driven dynamic resource allocation mechanism, building a cross-regional resource-sharing platform, implementing categorized guidance for resource allocation, strengthening internal management within universities, fully utilizing university resources, optimizing resource allocation, and enhancing the overall educational quality of universities.
Research on Image Segmentation Algorithm Based on Sparse Representation and Multi-Task Learning
The results of image segmentation greatly affect the accuracy and correctness of image recognition. In this paper, image segmentation is taken as the research focus, combing and introducing the principles of multi-task learning, image sparse representation and tracking algorithm. An image segmentation model based on sparse representation and multi-task learning is designed, and an attention learning network AL-Net based on multi-task earning is proposed. The loss function of multi-task makes the contour of the segmented image smoother. The image segmentation model constructed in this paper is applied to the image segmentation of cucumber disease leaves, when the number of images in the training sample rises from 80 to 120, the recognition accuracy of the model in this paper stably stays at 90%, and the average recognition rate of seven diseases, such as leaf spot and scab, is as high as 90.49%, and the average time consumed is only 7.68s, which makes the effectiveness remarkable.
Copyright (c) 2026 Aiwu Chen https://creativecommons.org/licenses/by/4.0/
Research on Artificial Intelligence Technical Support of WSR Methodology in Performance Management of English Teachers in Colleges and Universities
Performance management evaluation of university English teachers is of great significance for improving the current status of English teachers' capabilities, and the relative importance of evaluation indicators is a prerequisite for ensuring the accuracy of evaluation results. This article is based on the WSR system methodology, incorporating artificial intelligence technology into its causal framework factors, and utilizing research projects for quantification to construct a performance management model for university English teachers. Based on this, an evaluation indicator system for the performance management of university English teachers was established, and a comprehensive evaluation model for English teacher performance management was developed using entropy weighting and grey relational analysis. To further explore the factors influencing the performance management level of university English teachers, the fsQCA method was introduced to conduct a configurational analysis of English teacher performance management. The study found that the “logic” component in the WSR system methodology has a greater influence (with a weight value of 0.3458), and the comprehensive score for the performance management level of university English teachers in the selected cases fluctuated between 0.32 and 0.87 points. There are two configuration paths to promote high-performance management levels among university English teachers, both of which highlight the importance of research capabilities, specifically the efficient application of artificial intelligence (AI) technology. Therefore, university English teachers need to expand the application of AI technology and accelerate the intelligent reform of English teaching to achieve the development of high-performance management levels.
Copyright (c) 2026 Yingping Liang, Chengyue Lv, Haoyuan Li, Yishan Liu https://creativecommons.org/licenses/by/4.0/
Intelligent Tutoring System for Teachers' Digital Competence Improvement in China: A Systematic Literature Review
This paper conducts a systematic review of quantitative empirical studies from 2020 to 2025 on the integration of teacher digital competence enhancement with intelligent tutoring systems (ITS). A total of 84 high-quality academic articles were selected based on the PRISMA process. The focus is on the mechanisms through which diagnostic-push ITS influence teachers' digital skill development, with analyses grounded in key theories such as DigCompEdu, TPACK, and the Technology Acceptance Model. Central issues including teachers' technological self-efficacy, classroom technology integration, and instructional innovation are explored. The study found that most intelligent tutoring systems enhance teachers' digital adaptability in instructional design and classroom operations by leveraging feedback functions within learning management systems, artificial intelligence recommendations, and personalized intervention strategies. Notably, there was significant improvement in teachers' digital self-efficacy. Additionally, school organizational culture and information and communication technology (ICT) resources emerged as important moderating variables influencing the effectiveness of these systems. This paper constructs a causal model of “platform-capability-performance,” providing theoretical guidance and practical suggestions for cultivating teachers' capabilities in a digital educational environment. It also identifies the design flaws of current intelligent tutoring systems and future research topics, emphasizing the importance of localization adjustments and long-term evaluation.
Copyright (c) 2026 Rong Chen, Siti Zobidah Omar, Xin Shu, Yuchun Xiang, Su Xu https://creativecommons.org/licenses/by/4.0/
Research on Automatic Creation Method of Documentary Background Music Based on Deep Generative Modeling
Addressing issues such as lengthy production cycles and insufficient adaptability in the creation of background music for documentaries, this paper proposes an automatic composition method based on deep generative models. A source separation model based on RNN is designed to simultaneously process multi-track features in mixed audio. A bidirectional LSTM music generation model is constructed to leverage bidirectional temporal modeling capabilities to capture the global structural features of music, thereby optimizing the generation of melodies and chords. Experiments use classic documentary soundtrack segments as training data, and model performance is validated through spectral analysis, spectrogram comparison, and human subjective evaluation. Results show that after 2,000 iterations, the frequency distribution of the generated music converges with the sample music, and the bidirectional LSTM structure converges faster and produces better results than unidirectional models. In subjective evaluations, the model significantly outperformed the control model in terms of naturalness (4.35 points), creativity (4.52 points), and musicality (4.47 points), with a score difference of less than 0.5 points compared to real music.
Copyright (c) 2026 Hexi Wang, Mingjie Wang https://creativecommons.org/licenses/by/4.0/
A Study on the Application of Three-Dimensional Scanning Data Analysis Method Based on Convolutional Neural Network in Machining Inspection
In this paper, the mechanical workpiece point cloud data is obtained by 3D scanning technology, and the data preprocessing is realized by the extended Gaussian image clustering algorithm to align the point cloud data and other operations. Subsequently, the sliding least squares method is used to upsample the point cloud, combined with bootstrap filtering to denoise the data image and image sharpening and other processing. The improved Faster R-CNN processing detection method is designed by combining the multi-scale feature extraction structure, which improves the efficiency of machining detection by dimensionality reduction of the feature map of the previous layer, then feature extraction, and stitching the features extracted from different scale convolution kernels together. The test results show that the improved Faster R-CNN algorithm has obvious improvement in the aspects of large part size difference, occlusion, and accuracy, and can effectively solve the point cloud accurate alignment problem of local deformation of mechanical parts with strong robustness. At the same time, the performance of this model is significantly better than other comparative models in the process of target monitoring of construction machinery, and the accuracy of its AP50, AP75 and mAP indicators reaches more than 84%. In addition, the method in this paper can automatically screen the conformity of parts in machining inspection, which greatly reduces the workload.
Copyright (c) 2026 Tao Chen https://creativecommons.org/licenses/by/4.0/
The Interaction between Cognitive Style and Vocabulary Outputability of ESL Learners Based on Learning Behavioral Data
This study conducted an empirical study of the interactive relationship between cognitive style and vocabulary outputability of ESL learners based on the learning behavior data of 40 students from a MOOC platform. The data were obtained and analyzed through MongoDB, in which their learning behaviors were classified into field-independent and field-dependent cognitive styles, each with 20 students. Based on the research framework in this paper, the former includes expansion of resource clicks and interlecture exercises, while the latter includes courseware video clicks and student discussion behavior. As vocabulary output metrics were unavailable, the output values were derived through vocabulary output scale tests in terms of vocabulary quantity, vocabulary depth, vocabulary use precision, and vocabulary use automation degree. Based on the collected data, an empirical analysis of Pearson correlation coefficient values revealed significant positive correlations between ESL learners’ vocabulary outputability and their learning behavioral factors related to field-independent or field-dependent cognitive styles. The result indicates that differentiated teaching should be adopted to improve lexical teaching efficiency for ESL learners of different cognitive styles.
Copyright (c) 2026 Baiqiu Liao https://creativecommons.org/licenses/by/4.0/
A Study of Computerized Techniques for Health Monitoring and Risk Assessment of Athletes in a Big Data Environment in Sports
In this study, an athlete health monitoring system based on pulse wave signals is designed to address the problems of low data prediction accuracy and poor real-time performance of traditional athlete health monitoring techniques. The system collects and preprocesses the photoelectric volumetric pulse wave signals of athletes by applying a photoelectric volumetric pulse wave acquisition sensor device. Firstly, the differential thresholding method, the pulse map area method and the waveform fitting method are used to extract the characteristic parameters in the time domain, then the power spectrum analysis and the cepstrum analysis are used to extract the characteristic parameters in the frequency domain, and the HHT method based on the time-frequency hybrid domain is also used to extract the characteristic parameters of the pulse signals. Finally, the Hidden Markov Model is introduced to convert the pulse wave signals of the athletes that need to be evaluated into a sequence of physiological signal features, and the risk assessment of the athletes' health status is carried out. The results of the study show that the system in this paper can significantly differentiate the health status of healthy and diseased populations, and the fitted curve of the recognized healthy athletes' physical health level rises by 0.065, which is in line with their actual health status level. The system in this paper provides a scientific basis for injury prevention and sports performance optimization of athletes, and has important application value in the sports world.
Copyright (c) 2026 Xi Chen, Yaosheng Zhang, Menglin Chen, Yuquan Shi https://creativecommons.org/licenses/by/4.0/
Application and Exploration of Global Optimization Algorithm for Construction Projects in Project Cost Management
Construction quality, project progress, and cost are the three key control indicators in construction projects, which are interdependent and mutually influential. This paper constructs a multi-objective optimization model for schedule-cost-quality based on multi-objective optimization theory. Using an improved genetic algorithm to solve the problem, a Pareto solution set is obtained. Based on this, the entropy-VIKOR model is applied to make decisions on the solution set, identifying the optimal solution from the alternative options. This paper takes the YC Expressway Project as the research object for multi-objective optimization calculations in project cost management. Python programming is used for solution, resulting in an optimal construction period of 113 days, a reduction of 27 days compared to the planned 140 days. Costs were reduced by 1.4486 million yuan, and quality levels were improved. The optimization results validate the scientific and practical applicability of the project management multi-objective optimization model and the entropy value-VIKOR decision-making method.
Copyright (c) 2026 Weiwei Zhang https://creativecommons.org/licenses/by/4.0/
Research on the Technical Path to Optimize the Reconstruction of Large-Scale Historical Scenes in Historical Documentaries Using Parallel Distributed Computing Techniques
The continuous development of distributed computing technology provides a new path for high-precision reconstruction of historical scenes. In this paper, we propose a technical optimization framework based on parallel distributed computing for the problems of low efficiency and lack of clarity in the reconstruction of large-scale historical scenes in historical documentaries. By integrating digital elevation model (DEM) and 3D Gaussian sputtering (3DGS) methods, combined with the DAG scheduling mechanism of Apache Spark framework and multi-factor weighted resilient distributed dataset (RDD) caching strategy, the reconstruction speed and rendering quality of the scene are significantly improved. The experiments show that the parallel computing nodes are set to 15, the nodes adopt an interval of 1500m, and the number of single file computations is 11 times to obtain higher modeling efficiency. The ambiguity of scene reconstruction with the introduction of parallel distributed computing is reduced to less than 17%, and the average ambiguity is lower than 16%. The values of three evaluation indexes are better than those of the comparison algorithms.
Research on Two-Way Empowerment Mechanism between Higher Vocational Art and Design Professional Teaching and Enterprise Digital Content Operation Enabled by AIGC Technology
The collaboration between vocational colleges and enterprises in teaching art design and digital content operations, as well as mutual empowerment, is key to promoting the deep integration of vocational art design education and industrial development. This paper adopts a systems dynamics perspective to analyze the interactive development mechanisms between vocational college art design education and corporate digital content operations. An evolutionary game model is constructed to analyze the game evolution issues between vocational colleges and enterprises, providing a more intuitive illustration of the dynamic process of school-enterprise collaboration in the mutual empowerment between art design education and corporate digital content operations. Numerical simulation analysis of the game evolution model reveals that the revenue distribution and cost-sharing mechanisms of school-enterprise cooperation directly influence the willingness of both parties to participate actively. Government support and its underlying policies are more conducive to deep school-enterprise cooperation.
Copyright (c) 2026 Xiaoming Zhang https://creativecommons.org/licenses/by/4.0/
Research on the Sustainable Development Path of Green Finance Reform on Low-Carbon Transition of Enterprises Based on Multilayer Perceptual Machine Algorithm
This paper conducts a general study on the role, mechanisms, and characteristics of green finance in corporate low-carbon transformation and development from both theoretical and empirical perspectives. First, the paper reviews the concept of green finance and, based on the institutional characteristics and practical logic of green finance in China, proposes the basic research direction that green finance reform can facilitate corporate low-carbon transformation. Using panel data from 30 provinces (regions and municipalities) in China from 2014 to 2023, the study employs a multi-layer perceptron algorithm to calculate the annual green finance development levels across regions. A difference-in-differences model is then applied to test the effects of green finance policies on corporate low-carbon development. The findings indicate that green finance policies facilitate corporate low-carbon transformation, a conclusion that remains valid after testing for parallel trends, heterogeneity analysis, and robustness checks.
Copyright (c) 2026 Nanlan Du https://creativecommons.org/licenses/by/4.0/
Computer Simulation Analysis of the Impact of National Macro Policies on the Construction of Planned Key Projects in Lanzhou City during the “First Five-Year Plan” Period
This paper takes the key projects planned during the First Five-Year Plan period in Lanzhou City as the research object, and systematically analyzes the influence mechanism and heterogeneity characteristics of the national macro policies on the performance of engineering construction based on historical statistical data and computer simulation technology. The panel dataset is constructed, the double difference model is used to quantify the direct impact of policies, and the Heckman two-step method is utilized to conduct the robustness test. Construct the key engineering construction performance assessment system and evaluate the engineering construction performance level using hierarchical analysis. Combine the system dynamics simulation and mediation effect analysis to reveal the role path of macro policies. It is found that the national macro policy has a significant positive impact on the performance of key engineering construction in Lanzhou, and the performance of the projects supported by the policy focus is improved by 1.25 units on average (in terms of fuzzy evaluation value) compared with the similar projects not directly covered by the policy. The coefficient of the policy interaction term (DID) for the state-owned enterprise group is 1.50 and significant at the 1% level, while the coefficient of the policy interaction term for the non-state-owned enterprise group is only 0.30 and fails the significance test (t=0.85). Expansionary monetary policy and tight fiscal policy act on engineering construction through macro leverage ratio, economic growth rate and other intermediary variables, forming a differentiated transmission path.
Copyright (c) 2026 Lixin Wan https://creativecommons.org/licenses/by/4.0/
Research on the Optimization Scheme of Intelligent Algorithms in the Improvement of Cyber Attack and Defense Ability of Students in Applied Colleges and Universities
Enhancing students' cyber defense capabilities is a crucial measure to help them maintain their ability to identify threats and protect their legitimate rights and interests. In light of this, this study integrates and analyzes the influencing factors of cyber defense capability cultivation among students in applied universities, employs the entropy weight method to rank and combine these factors based on their importance, and optimizes the optimal combination tailored to students' individual circumstances. A two-level fuzzy comprehensive evaluation system is established to assess students' cyber defense capabilities. An improved particle swarm optimization algorithm is employed to solve the optimization model, followed by numerical simulation experiments. Experimental results indicate that compared to other improved multi-objective algorithms, the proposed algorithm demonstrates excellent convergence and diversity, and effectively achieves the enhancement of cyber defense capabilities based on this algorithm. Based on the actual assessment and feedback of this method from a certain applied university, measures to enhance network defense capabilities are proposed, namely, ensuring the effectiveness of network security ideological and political management through institutional safeguards, publicity and education, technical support, security management assessments, and the cultivation of high-quality management teams.
Copyright (c) 2026 Ziying Zhang, Pengfei Shi https://creativecommons.org/licenses/by/4.0/
A Study on Path Optimization of Digital Computing Methods in International Dissemination of Rural Revitalization Literature with High Quality Enhancement
The dissemination of rural revitalization stories helps to promote mutual integration and communication between countries and enhance China's influence in the global governance system. This project combines digital computing methods, constructs an evaluation index system, and uses fuzzy hierarchical analysis and grey multilevel evaluation model to assess the effect of international dissemination of rural revitalization literature. Then, combining correlation analysis and support vector machine model, it mines the relationship between each influencing factor and the international dissemination of rural revitalization literature, and thus proposes the optimization path of high-quality international dissemination of rural revitalization literature. The results show that the international dissemination effect of the sample rural revitalization literature is at an average level, with the overall score ranging from 1.57 to 3.96, among which 94.66% of the literary works have a score below 3.5. The factors that have the greatest influence on the international dissemination effect of rural revitalization literature are graphic type, cultural content and neutral attitude of content, with characteristic importance values of 0.089, 0.080 and 0.079 respectively.Based on this, this paper proposes the communication strategy of diversifying the dissemination subject, civilianizing the dissemination content, and innovating the dissemination method, in order to promote the high-quality international dissemination of rural revitalization literature.
Copyright (c) 2026 Yejin Lin, Weiwei Pan https://creativecommons.org/licenses/by/4.0/
Research on Food Processing Optimization and Nutrition Retention Based on Multivariate Statistical Analysis
This study explores the optimization method of food processing with potato crisps as an example. The experimental data were analyzed by principal component analysis using one-way and response surface tests with crushing force, oil content, L* value, sensory score, composite score and volatile components detected by electronic nose as indexes. The regression effect of the quadratic polynomial regression model established with the normalized composite score obtained from the principal component analysis as the response value was highly significant (p<0.01, R²=0.9591). Determine the optimal pretreatment process parameters of potato for the bleaching temperature of 92 ℃, bleaching time of 4min, slice thickness of 4mm and freezing time of 3h, in this condition to get the normalized composite score of 0.9633, close to the predicted value (0.9696), indicating that the combination of the principal component analysis and the response surface analysis method of the potato crisps processing process optimization of the comprehensive evaluation method is accurate and feasible. In order to optimize the food processing process, this paper proposes food nutrition retention measures from two aspects of cooking methods and processes, ingredient selection and procurement, respectively.
Copyright (c) 2026 Yonghua Fan https://creativecommons.org/licenses/by/4.0/
Research on the Whole Process Visualization Management of Engineering Projects Based on Digital Twin Technology
Large-scale construction projects with large scale, complex intersection of processes, fragmentation of information, labor shortage and other issues have challenged the traditional project management methods, and the demand for project visualization management is very urgent. This paper constructs a whole-process visualization management model of engineering projects based on digital twin technology, combines the earned value principle of cost dynamic correction with BIM technology, and proposes a method for controlling engineering construction cost. Finally, the method is applied to the construction cost analysis and Monte Carlo simulation experiment of a cross-sea bridge. Through the calculation of earned value parameters and the dynamic cost analysis based on the S-curve, it is found that when the construction of the project proceeds to the end of October 2022, the difference between the actual cost of the completed work and the planned cost of the completed work is 1,626,200 yuan. It shows that the project is characterized by cost overruns, slow progress and low efficiency. It can be concluded that the method of this paper has good applicability in the whole process visualization management of engineering projects.
Copyright (c) 2026 Ting Fan https://creativecommons.org/licenses/by/4.0/
Modeling and Research on the Relationship between Japanese Monster Culture and Folk Beliefs in a Big Data Environment
This paper aims to explore the intrinsic relationship between Japanese yokai culture and folk beliefs in order to gain a deeper understanding of cultural contexts and the foundations of belief systems. First, the collected data was preprocessed using the Jieba word segmentation tool and stopword removal methods. Textual data was converted into computable structured data using a vector space model. The TF-IDF algorithm was employed to extract text features and perform dimensionality reduction, resulting in features with simple and clear vector dimensions. Next, a multidimensional network of Japanese yokai culture and folk beliefs is constructed, and social network analysis is applied to investigate the strength of feature associations between Japanese yokai culture and folk beliefs. Association rule algorithms are then introduced to further explore the association patterns between Japanese yokai culture and folk beliefs, and the obtained association relationships are visualized. The study shows that the average shortest path in the relationship network between Japanese yokai culture and folk beliefs is only 1.05. This network is influenced by multiple factors such as yokai types, regional distribution characteristics, and historical periods, indicating that the network has high interaction efficiency, enabling the rapid dissemination of Japanese yokai culture. It is not easily disrupted by external factors, making the relationship network between Japanese yokai culture and folk beliefs more adaptable over time and conducive to cultural inheritance.
Copyright (c) 2026 Hong Zhang https://creativecommons.org/licenses/by/4.0/
Construction of the Evaluation System of Teachers' Intercultural Competence in the Context of Local Internationalization of Higher Vocational Education Based on the Entropy Value Approach
In the process of in situ internationalization of higher vocational colleges and universities, teachers' intercultural competence has an important impact on the level of in situ internationalization of schools. This paper analyzes the development situation of in situ internationalization in higher vocational education, takes teachers' intercultural competence as an important index to measure the internationalization level of education, and uses the entropy value method to construct the evaluation model of teachers' intercultural competence. The overall level of teachers' intercultural communication ability is investigated, and the evaluation system of teachers' intercultural ability is established at different levels. The combination of hierarchical analysis and entropy value method is used to determine the combination weights of teachers' intercultural competence, combined with the overall survey level of teachers' intercultural competence, and compared with the evaluation results of experts, to verify the reasonableness of the evaluation system of teachers' intercultural competence under the development of internationalization of higher vocational colleges and universities in the field established in this paper. The overall level of teachers' intercultural competence development under the development of internationalization in the field of higher vocational education is obtained by combining the experts' scores, which is 83.25 points, at a good level, utilizing the development of internationalization in the field of education in higher vocational colleges and universities.
Copyright (c) 2026 Chang Liu https://creativecommons.org/licenses/by/4.0/
A Study of the Fuzzy Effects of Psychological Stress on College Students' Performance in Physical Education Classes Based on Fuzzy Logic Modeling
To improve the effectiveness of physical education (PE) classroom teaching in higher education institutions, this study employs a fuzzy logic model to conduct a fuzzy assessment of college students' psychological stress and PE performance. Additionally, one-way analysis of variance (ANOVA) and independent samples t-tests are utilized to analyze differences in psychological stress and PE performance across gender, age, and major. Furthermore, correlation and regression analyses are conducted to explore the factors influencing college students' PE performance. The research results indicate that the psychological stress faced by college students primarily centers on future uncertainty and goal ambiguity, and significant differences exist in terms of age and gender (P < 0.05). The overall mean score for physical education class performance among the selected sample of college students is 4.01, which is at an above-average level. Significant differences in physical education class performance are observed across age, gender, and major (P < 0.05). Additionally, psychological stress was negatively correlated with physical education class performance, and competitive stress, time pressure, future uncertainty stress, and interpersonal conflict stress all had a negative impact on the total score of physical education class performance. This study provides a basis for improving college students' physical education class performance by addressing their psychological stress.
Copyright (c) 2026 Li Zhang https://creativecommons.org/licenses/by/4.0/
Computer Vision in the Media Field and Its Contribution to Content Creation
The integration and development of computer vision and art design in the media field is an important area of ongoing research and exploration in the humanities and society. This paper takes Generative Adversarial Network(GAN) as the research entry point, proposes its specific application in AIGC, and constructs an image art stylization model based on GAN to realize the migration of image art style for content creation in the media field. On this basis, corresponding to the needs of content generation and creation in the media field, the digital media content generation and creation technology based on computer vision technology is proposed to ensure the quality of the generated content. The content generation and creation technology in the media field proposed in this paper is applied to design public art in the public resting area of Guanfeng Village in Ganbei, Jiangxi Province, China, designing a public art gallery and evaluating it in comparison with neighboring villages such as Wangkou Village, Xiaoqi Village, Likeng Village and Huangling Village. Among the 25 adjective pairs in the public semantic evaluation, except for the adjective pair “abstract - figurative”, the value of positive adjective pairs in the remaining adjective pairs of Guanfeng Village is closest to 1, which brings the public a good artistic emotional experience. The value of all the remaining adjective pairs in favor of positive is close to 1.
Copyright (c) 2026 Xiaoguang Fan, Renkexin Li, Yanan Wu https://creativecommons.org/licenses/by/4.0/
Research on the Application of Multidimensional Data Mining Algorithms in Intelligent Management of Higher Education Institutions
Based on the campus card data of students at School A's Business School, this paper conducts data mining on student behavior from three dimensions: dining consumption levels, living patterns, and diligence in studying. An improved K-means algorithm is used to classify student behavior data. Then, conduct a correlation analysis of academic performance and behavioral indicators to identify patterns between behavior and academic performance. Use these insights to guide students in improving their poor behavioral habits and enhancing their learning efficiency, thereby improving their academic performance. This paper also improves the Apriori algorithm in association rules and compares it with other algorithms through experiments to verify the feasibility and high efficiency of the improved algorithm. The improved algorithm is then applied to student behavior analysis, revealing the correlation between student behavior habits and academic performance. The main factors influencing academic performance are identified as lifestyle behavior and learning behavior, while consumption behavior has a relatively minor impact on academic performance.
Copyright (c) 2026 Ye Zhang https://creativecommons.org/licenses/by/4.0/
Discussion on the Optimization Method of Modern Ink Figure Painting Composition Structure Based on Geometric Algorithm
Composition is an important factor in determining the aesthetic quality of modern ink figure painting, and the existing optimization algorithms in this field still have problems such as insufficient accuracy and rotational consistency, this paper proposes a composition optimization algorithm based on geometric algorithm. First of all, the color variety of the subject area and the color variety of the background area are chunked, and the color harmony feature is extracted by partition. Then, using the rule of thirds and the principle of visual balance as the principle, a deep convolutional neural network with VGG-16 as the model backbone is used to detect the saliency of the modern ink figure painting. In this paper, the accuracy of the composition structure optimization enrichment of modern ink figure paintings stays above 75%, and the attention and sensitivity to the three influencing factors of composition, ink color and texture, which are more important in the composition of modern ink figure painting style and aesthetics, are better, which not only provides a guarantee for maintaining the original painting style and aesthetics of the drafts after the optimization of the composition structure of the modern ink figure paintings, but also provides a theoretical basis for the modernization and transformation of the traditional paintings.
Copyright (c) 2026 Momo Feng, Ying Bai https://creativecommons.org/licenses/by/4.0/
Research on Multidimensional Evaluation Method of Digital Art Works Based on Multimodal Data Fusion Technology
Aiming at the problems that the traditional evaluation model does not realize the full use of information, resulting in poor generalization ability and low prediction accuracy, this paper combines the BPNN algorithm with the Self-attention mechanism, and puts forward a multi-dimensional evaluation model for digital art works based on SA-BPNN multimodal fusion. A multi-dimensional evaluation index system for digital art works is constructed, in which the weights of the indexes are obtained through the hierarchical analysis method. It is found that the aesthetic value is the most important in the final rating of digital art works, and emotional resonance and creative autonomy are also important evaluation elements. The study validated the weights of the AHP method by constructing a back-propagation neural network, and after validation, the prediction accuracy of the evaluation model reached 98.85%, which proved the effectiveness of the evaluation model. This paper provides theoretical support and tools for the realization of multi-dimensional evaluation of digital art works by using multimodal data fusion technology.
Copyright (c) 2026 Qiang Fu https://creativecommons.org/licenses/by/4.0/
Construction and Application of Personalized Learning Resources Based on Knowledge Graph in Higher Education Self-study Examination Environment
This paper first outlines the overall construction process of a course resource knowledge graph, analyzes the acquired dataset, and performs data cleaning and preprocessing. Relationships and entities are extracted from the dataset, and the course knowledge graph is constructed as triples and stored in the Neo4j graph database. Subsequently, semantic and difficulty information is extracted from exercise texts and learning records to obtain a comprehensive representation of students' responses at a given time. During the knowledge state update phase, unidirectional and bidirectional propagation modes are adopted for different relationships. Finally, against the backdrop of personalized learning in online education, a bipartite graph-based collaborative filtering course recommendation algorithm (CFBGR) is proposed, effectively mining the deep interaction information between learners and courses. The results show that the AUC and ACC values of the proposed model outperform those of some existing models, indicating that the research approach of considering knowledge point embedding in the knowledge tracking model has certain research significance. When recommending questions to students, the proposed method can tailor recommendations to each student's personalized learning level, ensuring that the recommended questions align with their individual needs. Additionally, the question recommendation results exhibit a high degree of accuracy and interpretability.
Copyright (c) 2026 Anxue Zhao, Xiaoting Jia https://creativecommons.org/licenses/by/4.0/
An Optimization Method Based on Multivariate Linear Programming for Urban Green Transportation Carbon Emission Control
In order to implement the concept of environmental protection and achieve the goal of urban green transportation, this study controls and optimizes the urban transportation carbon emission through the method of multivariate linear programming. This paper proposes a multivariate universe-interval linear programming algorithm, which is applied to the control of transportation carbon emissions. At the same time, the urban traffic carbon emission is measured, and the BP neural network is used to predict the traffic carbon emission, and the urban traffic carbon emission optimization model based on multivariate linear programming is constructed. The practical utility is tested by experiments. Taking the carbon emissions of residents' commuting in Q city as an example, the carbon emissions of Q city after the optimization of the model in this paper were reduced by 860 tons. The per capita carbon emissions of buses, subways, motorcycles and cars all show a decreasing trend. The average values of CO, CO2 and fuel emissions of this paper's model at traffic flow of 400 pcu/h and 900 pcu/h are much lower than other control methods. The model in this paper has a greater advantage in reducing carbon emissions from transportation.
Copyright (c) 2026 Dazhang Liu https://creativecommons.org/licenses/by/4.0/
Technical Innovation and Cultural Inheritance of Digital Calligraphy Art Creation
With the development of computer vision and digitization technology, automated generation of calligraphic fonts with specific styles has become a hot spot of research. Aiming at the current problems such as the strict requirements on the preliminary data collection work in the process of digitalized Chinese character art creation, this paper proposes a generative adversarial network model based on structural constraints. The structuralstyle generator, detail style generator and discriminator are used to generate, optimize and discriminate the images of calligraphic Chinese characters respectively, and finally complete the style migration to the target fonts. Analysis of the training and generation results of the model using the dataset reveals that, compared with other models, this paper's method, except for special and personalized fonts that lead to a lower recognition rate, the correct recognition rate of other fonts can be more than 70% or more, and at the same time, the digitized calligraphy art MOS value generated by the model focuses on the range of 7 to 9 points. Therefore, it can be considered that the method in this paper can simultaneously generate images of calligraphy fonts of multiple styles, and the different font styles do not affect each other, and the final generation of calligraphy fonts is also better than the existing font generation model.
Copyright (c) 2026 Weigang Fu https://creativecommons.org/licenses/by/4.0/
Innovations in Methodology for Analyzing Automotive Engine Vibration Signals Based on Wavelet Transform Techniques to Determine Maintenance Needs
This paper takes automotive engine vibration signals as its research object, proposes a wavelet transform analysis of automotive engine vibration signals, and further calculates the maintenance requirements of automotive engine vibration signals. It also analyzes the various operational status information contained in complex vibration signals. For engine vibration signals, traditional signal analysis techniques such as the Fourier transform have limitations in their analysis capabilities, particularly when dealing with non-stationary and nonlinear vibration signals. However, the wavelet analysis method, with its time-frequency locality characteristics, can completely avoid such contradictions. Under such circumstances, the analysis of engine vibration signals has a solid foundation and possesses fundamental functional significance. Based on this understanding, this paper conducts a specific and scientific study, establishing a scientifically reasonable signal analysis model. Using specific techniques such as wavelet packet decomposition and reconstruction, the paper performs a more in-depth and detailed analysis of the vibration signals of the Cummins KTA50 engine. Finally, the signal analysis process is validated through practical examples. The results demonstrate that wavelet transform technology can be used for the detection and analysis of engine fault information. Under certain conditions, it can provide more detailed and precise localization of fault information, enabling more accurate judgment of engine fault conditions and enhancing real-time reliability. From the technical perspective of high-order wavelets and adaptive analysis methods, wavelet analysis provides theoretical and methodological support for preventive maintenance of automotive engines and rapid, accurate fault diagnosis, holding significant application significance and value in both theoretical and engineering contexts.
Copyright (c) 2026 Hongqiang Liu, Xiaofeng Tan https://creativecommons.org/licenses/by/4.0/
Analysis of AIGC-Based Virtual Apparel Design System as An Alternative to Traditional Manual Design Process
Traditional manual design is often overly complex and time-consuming to measure, leading to its gradual elimination from the market. To achieve digital design in clothing, this paper aims to realize the personalization and virtuality of modern clothing design while reducing design costs. This paper designs a virtual clothing design system based on AIGC, combining web interface design processes to simulate customized design content, and introduces a particle-spring model for mechanical analysis. It explores collision detection and response methods between the human body and clothing using AABB bounding boxes. Through experiments with 30 samples, the reliability and accuracy of the proposed method are validated, and cotton and silk garments are selected for design rationality prediction. The experiment on the design rationality prediction of silk clothing showed that the correlation coefficient between the predicted values of the BP prediction model and the measured values was 75.22%, while the correlation coefficient of the method proposed in this paper was 90.33%. Thus, the method proposed in this paper has a better prediction effect on the design rationality of silk clothing. The AIGC-based virtual clothing design system has a better alternative to the traditional manual design process.
Copyright (c) 2026 Lina Zhao, Xiaoxuan Nie https://creativecommons.org/licenses/by/4.0/
Research on the Innovation Path of English Education Teaching Mode in Colleges and Universities Based on Differentiated Data Analysis
Differentiated data analysis promotes the precision of teaching management and the scientificization of educational decision-making, which is an important path to improve English education and teaching in colleges and universities. This paper gives a teaching model of English education in colleges and universities in combination with the problem-driven unit overall teaching concept. Apriori algorithm is used to mine the association rules of the data of SPOC course Comprehensive English, and the data are further analyzed differentially by K-means algorithm. The empirical analysis examines the impact and path of English education teaching model on students' academic performance. According to the path analysis, video learning resources (0.317) have the greatest impact on students' subjective attitudes, pre-course video learning resources determine learning activities, and teachers' classroom teaching is an important part of knowledge internalization. There is a relatively large correlation between the use of innovative teaching models and students' academic performance p < 0.05.
Copyright (c) 2026 Qingyan Ge https://creativecommons.org/licenses/by/4.0/
Path Design for Improving English Learners' Discourse Organization Skills Using Transfer Learning Models
This paper takes “transfer learning” as the research theme. Focusing on the issue of “learners' discourse organization ability”, it reveals the process of English transfer and innovative learning in the light of the teaching objectives, the transfer learning model and the concept of innovative thinking. Through the questionnaire survey and regression analysis, we explore the influence of English transfer and innovative learning model on English learners' regression analysis, we explore the influence of English transfer and innovative learning model on English learners' discourse organization ability in the development of English teaching. The study shows that the standardized regression coefficients of the three measures of discourse organization (texplicative discourse step, the number of small sentences in the evaluative discourse step, and the discourse markers) in the English Transfer Innovative Learning Mode are 0.705, 0.256, and 0.188, respectively, which are highly correlated with the speaking performance. Moreover, it showed a significant difference (p<0.05) between the high, medium and low subgroups.
Copyright (c) 2026 Fang Gu https://creativecommons.org/licenses/by/4.0/
Construction and Practical Research on the Dynamic Analysis Model of College Students' Employment Data in Higher Education Institutions under Big Data Environment
In the context of big data, college students face numerous challenges and opportunities in terms of employment. The processing of employment dynamic data suffers from issues such as poor employment trend prediction and weak precision in employment services. To address these issues, this paper employs big data to quantitatively process employment data. Using the K-means clustering algorithm, the paper determines the number of clusters in the employment data and completes the data clustering process. It primarily calculates the degree centrality parameters of network nodes to identify key points in the employment data required by users, thereby achieving employment data visualization. Based on time series analysis methods, the paper constructs a dynamic analysis model for college student employment data to analyze and predict employment trends among college students. Employment trend prediction practices are conducted using employment data from a certain higher education institution from 2017 to 2024 to predict the employment trends of college students in 2025. The employment index for the coming year in 2025 continues to show cyclical fluctuations, peaking in the fourth week (spring recruitment period), then rapidly declining, and recovering and rising to its highest point after the 30th week (autumn recruitment period). The results of the employment trend prediction generally align with actual social conditions.
Copyright (c) 2026 Meili Zhao https://creativecommons.org/licenses/by/4.0/
Selection of Heavy Metal Detection Technologies and Improvement of Operational Efficiency in Food Enterprises for Whole Life Cycle Costs
As the global industrialization process accelerates, environmental pollution continues to worsen, and heavy metal contamination has become one of the top priorities in food safety. Food companies must prioritize heavy metal testing as a critical component of their food safety regulatory efforts to ensure food safety. Therefore, this paper analyzes the optimal path for food companies in selecting heavy metal testing technologies and the process of enhancing their lifecycle operational efficiency from a dual perspective of food safety and business operations, incorporating the core theoretical principles of lifecycle cost management. A framework has been established to quantify and analyze cost factors across all stages of the lifecycle, including equipment procurement, post-purchase staff training and daily maintenance, and final equipment disposal. This framework provides scientific recommendations based on cost analysis for decision-makers in food companies when selecting heavy metal detection technologies. Additionally, from the perspective of economic performance ratios such as detection costs, it offers practical and actionable suggestions for selecting appropriate heavy metal detection technologies. Analysis has shown that adopting a technology selection process based on lifecycle cost analysis can effectively improve heavy metal detection quality while reducing overall corporate costs. Through the exploration of combining and integrating technological innovation and management innovation, this study provides valuable methods and insights for promoting the modernization and transformation of the food industry.
Copyright (c) 2026 Xueting Liu https://creativecommons.org/licenses/by/4.0/
Research on Adaptive Traffic Signal Control Algorithm for Intelligent Transportation
Multi-intersection traffic signal control is the key to improve the overall traffic efficiency. In order to more realistically reflect the real-time traffic conditions at intersections, this paper first establishes a multi-phase real-time control model using the average delay time as the objective function, so as to obtain the cycle of each intersection with the green light time of each phase. Then the communication capability between multiple intelligences is utilized to coordinate the execution of their own timing schemes. Simulation experiments are run in Vissim platform to verify the effectiveness of the adaptive traffic signal control algorithm proposed in this paper. The results show that the adaptive traffic signal control algorithm proposed in this paper is significantly better than the fixed timing and traffic weight timing algorithms in three aspects, namely, queue length, vehicle waiting time and average vehicle speed. The average queue length, the average waiting time of vehicles on each road and the average speed of vehicles on each road are 4.12m, 4.67s and 29.17 km/h, respectively.
Copyright (c) 2026 Chunhong He, Bin Ren https://creativecommons.org/licenses/by/4.0/
Optimization and Simulation Research on Landscape Design of Lanzhou Waterwheel Based on Three-dimensional Modeling Technology
This study proposes a method for the digital reconstruction of the Lanzhou waterwheel landscape based on 3D laser scanning technology. First, point cloud data of the waterwheel surface is obtained through multi-station laser scanning. Public control points are used to align and stitch the point clouds, and feature measure segmentation technology is employed to handle complex scenes. For feature extraction, the AKAZE algorithm is selected to construct a nonlinear scale space, combined with the M-LDB descriptor to enhance rotation and scale invariance. The Flann-based matching process is improved through grid-based preprocessing and group RANSAC model optimization to enhance feature matching accuracy. Based on the point cloud geometric framework, the seed point matching method is employed to achieve precise texture mapping, resulting in a high-fidelity 3D model. Performance validation shows that the model accuracy score in subjective evaluation is 94.85 points, with lighting and shadow effects exceeding 93 points. The AKAZE algorithm reduces skeleton extraction time by 12.1–18.0%. The total modeling time of the model is only 15.24 minutes, which is up to 74.7% faster than the comparison method. The average feature point distance error is 2.33 m, a reduction of more than 57.6%. The spatial stereoscopic level reached 84.12%, significantly higher than the 26.34–63.16% achieved by traditional methods.
Exploring the Application of Knowledge System of Finance Discipline Constructed on the Basis of Knowledge Graph in College Education
Applying disciplinary knowledge mapping to teaching in colleges and universities, taking the knowledge system of financial disciplines as the entry point, based on the disciplinary knowledge mapping technology, using the CRA model and employing the top-down method to construct a financial disciplinary knowledge map with the characteristics of professionalism, openness, interactivity and intelligence. Through the questionnaire survey, the application of the knowledge graph visualization system is analyzed mainly from three dimensions: system design, learning support, and learning effect. The results show that 60% of the users in the three dimensions of system design, learning support and learning effect are very satisfied and satisfied, and no more than 30% of the users are dissatisfied. The knowledge graph of financial discipline constructed in this paper can adequately represent the knowledge of the field and meet the application requirements such as supporting the deep learning of knowledge in the financial field.
Copyright (c) 2026 Li He https://creativecommons.org/licenses/by/4.0/
Combining AIGC and Virtual Simulation Technology to Enhance the Personalization of Apparel Designs
With the increasing demand of consumers for personalized experience and fast response, the design and production model of traditional apparel companies is facing serious challenges. In order to adapt to market changes and maintain competitive advantages, the industry urgently needs to adopt emerging technologies. In this paper, based on CycleGAN, we make the generated images more natural by improving the network model and adding background optimization loss to achieve clothing image style migration and fusion. Based on the LoRA-DAE framework, the method of LoRA integration into the stable diffusion model is proposed, which enhances the model adaptation ability. A fashion clothing model dataset is established to provide data support for the model. Through comparative experiments to evaluate the viewing experience of the method proposed in this paper, the model of this paper scores 27.1585, 14.4955, 17.6485, and 13.8486 on overall, shape, shadow, and detail texture, respectively.Meanwhile, it is also superior to other algorithms on subjective scores, and the average score of users' subjective evaluations is 27.9588.The model of this paper is mounted on the apparel personalization applet, and the experimenter's evaluation of this paper's apparel personalization apparel is conducted. When the model of this paper is installed on the apparel personalized customization app, the “gaze duration” of the experimenters on the apparel customization app is 1834.6985ms, which is 41.7156% higher than that of the control group, and the mean value of the subjective experience rating is 4.2663, which is higher than that of the control group, and based on the model of this paper, the model provides a higher psychological experience.
Copyright (c) 2026 Xi Lu https://creativecommons.org/licenses/by/4.0/
A Cloud Computing-Based Framework for Enterprise Marketing Data Analysis and Customer Behavior Prediction in the Digital Economy Era
This paper first establishes a data collection and quality control mechanism, combining statistical analysis tools and data mining techniques to enhance data value. It defines the boundaries of customer behavior, introduces survival analysis theory, and characterizes the temporal distribution patterns of customer churn. Based on the Markov chain model, it designs a transition probability matrix and prediction framework for customer behavior to capture the dynamic characteristics of state transitions. Using three years of micro-store order data from a certain snack chain brand as a sample, the consumption characteristics of 1,454 high-frequency customers are extracted. Survival analysis is combined to validate the negative correlation between customer system association and survival time, and Sharpley's additional explanatory value is used to analyze the key influencing factors of customer ordering and order cancellation behavior. In the survival experiment, the higher the association degree among units within the customer system, the worse the customer's survival status. Customers with significant fluctuations in consumption amounts exhibit stronger survival resilience, with the longest survival time reaching 23,731 hours. Feature A9 has the most significant positive impact on customer ordering intent, while feature B8 has the most significant negative impact on customer order cancellation intent, making them key variables for precise intervention in corporate marketing decisions.
Copyright (c) 2026 Ruimei Wang https://creativecommons.org/licenses/by/4.0/
Optimization Design of Intelligent Pronunciation Assessment System Based on Speech Recognition Technology in Higher Vocational English Classroom Teaching under the Background of Industry-Teaching Integration
The in-depth development of the integration of industry and education requires that English teaching in higher vocational colleges and universities be equipped with more objective and convenient methods and means for students' speech recognition, detection and assessment. In this paper, a computer-aided English pronunciation assessment system is established by utilizing speech recognition technology and combining the characteristics of English pronunciation with the verification of speech segments, the cutting of language signals and the assessment of pronunciation. MFCC is selected as the voice feature parameter of the proposed system to meet the needs of pronunciation model training. For accent assessment, the neural network-based GOP method is introduced as a deep learning-based accent assessment method to construct a theoretical modeling method for the English pronunciation assessment system. Compared with the traditional methods, the modeling method proposed in this paper consistently maintains an average error between 0.001-0.1 on the automatic assessment of English spoken pronunciation quality, which demonstrates a superior performance of automatic assessment of spoken pronunciation quality.
Copyright (c) 2026 Tingting He, Jiguo Yao, Yuhan Zou https://creativecommons.org/licenses/by/4.0/
Intelligent Scheduling Algorithms and Resource Allocation Methods in Library Digitization Management
Addressing the issue of intelligent scheduling and resource allocation optimization for library digital resources, this paper first designs a cloud-service-based library database resource model framework and studies the required multi-objective combinatorial optimization system based on the knapsack model. Then, an optimization configuration model based on a multi-agent genetic algorithm is designed, combining a multi-agent system with local perception characteristics with a globally efficient genetic algorithm. The fitness of individuals is selected as the energy of the multi-agent system to achieve multi-objective collaborative optimization of digital resource allocation. Finally, library digital resource data from X University Library from 2016 to 2024 is selected for empirical analysis. The empirical analysis results indicate that the proposed model can reasonably perform multi-objective collaborative optimization of resource allocation quantities for X University Library. The multi-objective evaluation value of digital resource allocation at the library increased by 0.22 between 2016 and 2024. Additionally, the proposed model demonstrates significant advantages in terms of digital library resource allocation efficiency, enhancing the collaborative level and efficiency between multi-objective evaluations of digital resources. This lays the foundation for future research on digital resource allocation in other domains of higher education institutions.
Copyright (c) 2026 Jiaojiao He, Yu Zhao https://creativecommons.org/licenses/by/4.0/
Graphics Algorithm Based Virtual Reality Immersive Interactive Interface Design for International Chinese Language Teaching and Learning
This paper addresses the issue of cultural experience deficits in international online Chinese language instruction by proposing a layered optimization method for VR immersive interactive interfaces based on computer graphics algorithms and human visual characteristics. By analyzing human color sensitivity, spatial perception, and multi-channel characteristics, a layered optimization model centered on visual perception intensity is constructed, and a genetic algorithm is employed to solve the layout of interface elements. The experimental section focuses on Chinese language teaching scenarios, determining that the optimal combination is a feature space capacity of 128MB (accuracy rate of 93.18%) and a dictionary space of 512MB (accuracy rate of 78.89%). When the weight parameter is set to 0.35, the recognition accuracy rate reaches 97.23%. Quantitative analysis of 15 teaching elements (e.g., the 3D model of the Hall of Supreme Harmony, cultural explanation subtitles) shows that the core teaching elements (A1/A8) have a visual communication index (VCI) > 3700, and high-intensity visual perception elements (VPI ≥ 4.5) are concentrated in the core teaching area (accounting for 48%). Among the 1,520 element layouts, 32% of high-intensity areas carry 87% of high-VCI elements. The proposed method significantly outperforms existing methods in Chinese cultural image processing, with an average gradient of 6.65–8.05, 1.9–2.8 times higher than contrast methods, a PSNR improvement of 29.7–40.2%, a peak of 21.23, and an MSE of only 2.68%–4.80%, which is 1/7 of contrast methods.
Copyright (c) 2026 Xiaoyan Wang https://creativecommons.org/licenses/by/4.0/
Quantitative Research on the Influence Mechanism of Digital Transformation of Universities on Regional Economic Competitiveness Based on Multivariate Statistical Analysis
Aiming at the problems of insufficient systematic assessment system of digital maturity of colleges and universities and in-depth exploration of regional impact heterogeneity in existing research, this paper constructs a maturity assessment model containing three dimensions of digital strategy, digital technology, and digital capability. Based on the panel data of 25 provinces in China from 2015 to 2024, the hierarchical analysis method is used to determine the weights of the indicators. Combining the spatial Durbin model and the fixed-effects model, the path of the influence of the digital maturity of colleges and universities on the competitiveness of the regional economy and the characteristics of regional heterogeneity are systematically revealed. The overall digital maturity of colleges and universities is at the medium-high level (the mean value is 0.61), close to the governance level, but the differences between regions are significant. The EDM coefficient of the western region reaches 10.389 (t=2.753, 1% significant), far exceeding that of other regions, while the eastern region focuses on synergistic effects with industrial upgrading, the central region focuses on optimization of resource allocation, and the northeastern region is limited by traditional path dependence.
Copyright (c) 2026 Yan Hong https://creativecommons.org/licenses/by/4.0/
Research on Personalized Learning Path Construction of College Short Video Platform Based on Dynamic Content Optimization Algorithm
The study designed a dynamic genetic algorithm based on dynamic content with multi-adaptation by setting stopping criterion, cost function optimization and other strategies, and constructed personalized learning paths in short video platforms in colleges and universities by this algorithm. In order to more accurately realize the personalized learning path recommendation, the learner model and the learning resource model are constructed, and at the same time, according to the mapping relationship between the two, multiple objective functions such as the cognitive level of the learner and the learning style are determined. Combined with the improved optimization algorithm, the personalized learning path construction problem is solved to complete the personalized learning path construction under the short video platform. The algorithm in this paper iterates 140 times, and the objective function converges to about 12, which is faster and better than the convergence speed of the comparison algorithm. Through personalized learning path recommendation, the average grade of students in the experimental group is improved by 7.4 points compared with the control group, which is a significant difference. Under the personalized learning path recommendation, the average time for students to learn the knowledge points in the college short video platform drops dramatically, and the average learning time is only 65.36 min. the multidimensional scores of the students for the personalized learning path are 4.12-4.72, in which the path needs to be further improved for the improvement of learning efficiency.
Copyright (c) 2026 Yue Zhou, Xi Lu https://creativecommons.org/licenses/by/4.0/
A Markov Chain Model-based Study of the Impact of College Career Planning Courses on Graduates' Career Paths
Improving the employment quality of college graduates requires a clear understanding of their spatial distribution and influencing factors. This article takes Jiangsu Province, China, as its research object and explores the impact of career planning courses on the employment paths of college graduates. A graduate employment quality evaluation system was constructed, and the entropy method was used to assign weights to the indicators. The Dagum Gini coefficient, kernel density estimation, and Markov chain model were comprehensively used to explore the spatial differences, distribution dynamics, and influencing factors of the employment quality of college graduates in Jiangsu Province. The results indicate that the overall differences in the employment quality of college graduates in Jiangsu Province are narrowing, with regional differences being the primary cause of these differences. Additionally, the overall employment quality of graduates is accompanied by polarization. In terms of spatial distribution patterns, the overall distribution shows a “high in the east and west, low in the central and southern regions” trend, with the spatial pattern shifting from a “east-west” distribution to a “southeast-northwest” direction. In terms of distribution dynamics, there is a stable club convergence phenomenon in the employment quality of college graduates, and there is a “spatial spillover” effect in its development process.
Copyright (c) 2026 Caiying Zhong, Xiaohui Lin https://creativecommons.org/licenses/by/4.0/
An Accurate Assessment Model of Big Data-Based Physical Education Programs in Colleges and Universities for Student Health Enhancement
In this paper, we design a research framework for college student health level data that integrates K-means clustering method and Apriori algorithm to establish an assessment model between physical education courses and student health. The maximum minimum distance and inter-sample density are used to improve the sample point categorization of K-means clustering and select more appropriate initial clustering centers. Apriori, a linkage linkage profiling class algorithm, is used to deeply mine frequent item sets at multiple levels to improve modeling efficiency and accuracy. It was found that five strong association rules were mined for each of male and female college students, with support and confidence ranges of 9.214%-10.925%, 57.071%-90.426% (male) and 11.351%-15.713%, 55.423%-67.153% (female), respectively. Male and female college students were clustered into 5 categories according to their physical fitness test scores respectively. Physical fitness level = 72.735 + 0.689*exercise intensity + 0.527*exercise time + 0.641*exercise frequency.
Copyright (c) 2026 Qian Hou https://creativecommons.org/licenses/by/4.0/
Practical Exploration of Digital Twin Technology to Construct a Teaching Environment Combining Virtual and Real in Civic and Political Education
The rapid development of information technology has made digital twin technology a key driver of educational transformation. This paper combines high-performance sensors and other hardware with feature extraction algorithms and other technologies to construct an online immersive teaching system that integrates reality and virtuality, thereby achieving the digitization and virtualization of ideological and political education. The TSDF algorithm, Neural Recon algorithm, and Marching Cube algorithm are used to process spatial data at high speed, and ThingJS and GIS technology are employed to convert between virtual and real coordinates, thereby enhancing the interactive performance of the model. Research findings indicate that the model's accuracy, completeness, and average value metrics reach 0.3013 mm, 0.1718 mm, and 0.4011 mm, respectively, with iteration loss values ranging from 2.50 to 9.98, outperforming comparison models in reconstruction performance. When the model is used to assist ideological and political education, the experimental class demonstrates significantly higher ideological and political proficiency across five dimensions compared to the control class (p < 0.01).
Copyright (c) 2026 Xuefei Wang https://creativecommons.org/licenses/by/4.0/
Research on Spatial Geometric Transformation and Path Planning Problems in Plant Landscape Design Based on Topology Optimization Algorithm
This paper designs the overall architecture and operational workflow of the system based on the hardware design of traditional digital plant landscape planning systems. Considering the system's flexibility, network operation mode, and operational security, the system's topological structure is determined. The system database is established from two aspects: data relationships and data types. This enables the system to perform digital plant landscape planning functions. On one hand, the system can achieve spatial geometric transformations of plant landscapes; on the other hand, it can optimize landscape paths based on an improved fish swarm algorithm. The landscape association values, minimum ecological damage values, and maximum spatial utilization values obtained from the plant landscape spatial element layout optimization model are superior to the target values of the actual scheme. Compared with the hybrid genetic algorithm, the path planning method based on the improved artificial fish school algorithm reduces the runtime by 34.4% and achieves better optimal solutions, path lengths, and smoothness than the hybrid genetic algorithm. This system can effectively solve the problems of spatial geometric transformation and path planning in plant landscape design.
Copyright (c) 2026 Hanhan Zhou, Peiyun Bie https://creativecommons.org/licenses/by/4.0/
Research on Cultural Characteristics and Environmental Adaptability in Public Space Using Artificial Intelligence-Assisted Design
As the main activity place to meet the public needs of residents in the city, the overall design of public space presents cultural characteristics and environmental integration, which is closely related to the satisfaction of the residents and the appearance of the city. This paper proposes a collaborative filtering-based public space layout algorithm by combining the distribution probability of design features and user interest characteristics. The deep neural network is used to extract the features of different spatial functional requirements and spatial layout parameters from the distribution structure and distribution pattern of the layout space. After obtaining the overall layout scheme and layout parameter features, the optimal layout scheme is determined by minimizing the energy function in the layout method. The graph-driven MH layout optimization (GD-MH) algorithm is improved to optimize the spatial layout, and a public space layout design method based on the GA-MH algorithm is formed comprehensively. The designed method is able to optimize the layout scheme with an interference of 0mm compared with similar methods.
Copyright (c) 2026 Weiwei Hou https://creativecommons.org/licenses/by/4.0/
Reinventing the Global Services Value Chain under the Wave of Digitization: An Analysis Based on Nonlinear Regression Models
Under the impact of digitalization wave, it is both a challenge and an opportunity for the global service industry value chain. Combined with the current development status of digital economy, it is proposed to use the entropy weight method to obtain the value of the development level of the former digital economy, while the value of the global services value chain is obtained by the trade value added method. The research variables are set, the data sources are determined, and the influence relationship between the two is explored using a nonlinear regression model. There is a significant positive correlation between the level of digital economy development and the status index of the global services value chain, and the status index of the global services value chain rises by 0.042 units for every unit increase in the level of digital economy development, which shows the interaction between the two, and provides guidance and reference for the reengineering of the value chain of the global services industry under the wave of digitization.
Copyright (c) 2026 Gaihong Lu https://creativecommons.org/licenses/by/4.0/
Research on Tourist Satisfaction Improvement Path Based on Multiple Regression Analysis in Tourism Management
As the core service object of the tourism industry, tourists' satisfaction with a tourist destination is an important influencing factor for the sustainable development of the tourism industry there. This paper proposes a set of tourist satisfaction index system containing a total of 20 secondary indicators from five perspectives: feeling experience, emotional experience, thinking experience, action experience, and association experience. And set the satisfaction of tourists' experience as the dependent variable, and 5 level 1 indicators as the independent variables. At the same time, IPA analysis was selected as the method of measuring tourist satisfaction, factor analysis as the method of testing the reliability and validity of the indicator system, and multiple linear regression prediction model as the method of analyzing the intrinsic relationship between the dependent indicator and multiple independent indicators. Taking K scenic spot as the experimental object, the potential relationship between dependent and independent variables is analyzed based on sample data. Among them, emotional experience has the greatest positive effect on tourist satisfaction, with a regression coefficient of 1.677. Accordingly, in tourism management, the emotional experience of tourists should be the first consideration, and the overall satisfaction should be promoted by providing a good emotional experience.
Copyright (c) 2026 Yanlong Wang https://creativecommons.org/licenses/by/4.0/
Discussion on the Integration of Ethnic Art Teaching and College Curriculum Civics Based on Augmented Reality Technology
The application of AR technology provides a new direction for integrated teaching in colleges and universities. In this paper, we build a national art and course ideology teaching system integrating augmented reality technology to improve students' sense of national cultural identity. Based on Cardinal spline curve to complete the design of the movement path of ethnic dance movements. Combined with the visual synchronous localization and mapping (SLAM) localization tracking and the improved natural feature tracking registration of the optical flow method, it improves the positioning accuracy of the dance movements and realizes the multi-view observation of the virtual dance characters. It is shown that the motion target tracking accuracy of this paper's method reaches 0.970 and the success rate is up to 0.993, which is better than the comparison algorithms. The system has only 2-3 recognition errors in 2 out of 7 tasks, and the average score of learning effect is greater than 10. After applying the system to assist learning, the experimental class students' interest and ability to learn ethnic art significantly increased at the 0.01 level, effectively realizing the organic penetration of immersion perception of ethnic culture and curriculum politics.
Copyright (c) 2026 Ansheng Huang https://creativecommons.org/licenses/by/4.0/
Research on Teacher Education Curriculum Design Combined with Genetic Algorithm Optimization from the Perspective of Basic Theory of Education
This paper is based on the perspective of basic educational theory and proposes a course design scheme based on a combinatorial genetic algorithm to address the current issues in higher education teacher education course design. First, based on the relevant factors and constraints of course design problems, the objective function, mathematical model, and fitness function for this study are determined. To address the local optimality issue in traditional genetic algorithms, the simulated annealing algorithm (SA) is introduced to optimize the genetic algorithm, thereby completing the course design work for teacher education in higher education institutions. Numerical experiments are then conducted to conduct in-depth validation and analysis of the course design for teacher education. After introducing the simulated annealing algorithm (SA), the convergence speed of the genetic algorithm (GA) was significantly improved. After 100 training runs, the algorithm could obtain the optimal solution to the course design problem, verifying the optimization effect of the simulated annealing algorithm on the genetic algorithm. Additionally, under the influence of the algorithm in this paper, the satisfaction rate of student course selection remained at 100%, demonstrating the practical application value of this algorithm in teacher education course design.
Copyright (c) 2026 Qin Zhou https://creativecommons.org/licenses/by/4.0/
Exploring the Quantitative Relationship of Physical Activity on Group Health Benefits Based on Regression Analysis Models
This study takes 1,000 college students in a university as the research object, and explores the quantitative relationship of physical exercise on group health benefits based on multiple linear regression model and Spearman correlation analysis. By setting health literacy as the dependent variable, exercise space, behavior, equipment and individual willingness as independent variables, and combining descriptive statistics and demographic difference analysis, the study systematically analyzed the mechanism of the influence of different exercise dimensions on health benefits. Regular exercise behavior had the strongest correlation with health literacy (r = 0.419, P = 0.000), followed by individual willingness (r = 0.396, p = 0.000), while exercise space (r = 0.178, p = 0.009) and equipment (r = 0.233, p = 0.002) were supporting factors. Multiple regression analysis showed that restricted exercise space, irregular exercise and no exercise equipment significantly reduced the probability of high health literacy, while the probability of high health literacy was much higher in the voluntary exercise group than in the unwilling group (p < 0.001).
Copyright (c) 2026 Huan Huang, Wei Wu https://creativecommons.org/licenses/by/4.0/
An Intelligent Teaching Aid System for English Linguistics Courses Based on Computer Generated Dialogue System
Information technology is of great significance in the curriculum setting and implementation of college English. Traditional college English pays more attention to the extension of the application of multimedia facilities, which is characterized by the problems of untimely updating of content, heavy workload of teachers' preparation and insufficient reinforcement of grammatical context. In this paper, a reinforcement learning-based dialog generation model is proposed based on reinforcement learning theory, which fully considers the one-to-many relationship in dialog data, i.e., there may be multiple reasonable replies for each user input. Then on the basis of the model, an intelligent teaching dialogue system is further developed to realize assisted teaching for English language courses. Through the comparison experiments and case studies of different baseline models in the dataset, it is found that the dialogue generation model proposed in this paper performs better than the other baseline models in various indexes, while the intelligent system can still support about 55% of the users to complete the request when the users reach 700 in the functionality test. Finally, the statistical analysis of the performance in the study case shows that the intelligent dialog generation system proposed in this paper can affect the actual effectiveness of teaching and learning.
Copyright (c) 2026 Li’ao Luo https://creativecommons.org/licenses/by/4.0/
Construction of Multi-Channel Teaching Effect Evaluation System Based on Deep Learning and Enterprise Cooperation Operation Practice
The continuous development of deep learning technology has driven the intelligentization of classroom teaching. This paper proposes a real-time teaching evaluation system by combining CVAE-GAN image enhancement with the lightweight YOLOv5 object detection framework. To address the issue of blurred student expressions in classroom scenarios, a conditional variational adversarial generative network is employed for facial reconstruction. The SE attention mechanism and GSConv module are integrated to optimize the YOLOv5s network, enhancing detection performance while maintaining the number of parameters. Experiments show that the improved YOLOv5 model achieves an mAP of 79.78% and an F1 score of 0.82. The accuracy rates for the five facial expressions are 97.15%, 92.63%, 92.74%, 90.82%, and 91.44%, respectively. The system can precisely identify changes in students' facial expressions and classroom attention levels in one-minute intervals, providing teachers with references for instructional adjustments.
Copyright (c) 2026 Yang Wang, Jing Ma https://creativecommons.org/licenses/by/4.0/
Multidimensional Data Mining Model Analysis of Financial Structure Optimization of Universities in Government Accounting Comprehensive Budget Performance Management
Under the background of government accounting comprehensive budget performance management, this study focuses on the optimization of financial structure of colleges and universities, and constructs a set of financial decision-making model system based on multi-dimensional data mining. By integrating high-precision financial data such as budget accounts, project expenditures, scientific research funds, tuition income and expenditure of universities. Innovative fusion of three types of data mining techniques. Multi-dimensional association rule mining reveals inter-departmental expenditure associations, cost composition laws, project hierarchy differences, etc. ID3 decision tree algorithm realizes financial risk classification and budget execution evaluation.DBSCAN density clustering divides student groups by the amount of outstanding fees and supports poor student classification. The data read/write efficiency of the proposed method is over 42bit/s, which is 200% higher than the benchmark method, and the redundant rules are reduced by 60%. And 415 strong association rules are generated (e.g., conference fee → postal fee, confidence level 0.6125, enhancement 1.98).The AUC of the three poverty categories classified by the DBSCAN model exceeds 0.98, which indicates that the model is more accurate and achieves a better classification effect. The decision tree model accurately quantifies the poverty class (0.247 gain in per capita annual household income of the root node), providing a data kernel for scientific allocation of resources.
Copyright (c) 2026 Qingquan Huang https://creativecommons.org/licenses/by/4.0/
Using Augmented Reality to Enhance Immersive Learning Experiences in Dance Education
With the widespread adoption of information technology, the internet, and mobile devices, there has been a significant transformation in learning methods and teaching approaches. In the field of sports dance education, leveraging modern technology to innovate teaching methods and tools can provide richer and more diverse learning experiences while fostering students' comprehensive abilities. This study follows the mechanism by which augmented reality technology enhances dance learning outcomes, utilizing the OpenCV image algorithm library to obtain depth images of dance movements. A random decision forest is employed to classify human body parts, thereby generating a human motion skeleton. Using the obtained data, a dance teaching platform based on augmented reality technology is established. The cosine similarity function is employed to estimate the dance posture of the left arm, for example. By comparing the posture correlation parameters between standard dance movements and the tested dance movements, the mean differences between the two sets of parameters are -0.00154, 0.01537, and 0.00372, indicating that augmented reality technology effectively estimates dance movement postures. A survey was conducted to assess students' satisfaction with the dance education model using augmented reality technology. The mean satisfaction scores for the course, teaching, platform, expectations, perception, and overall satisfaction were 4.3283, 4.4639, 4.0534, 4.4854, 4.4822, and 4.414, respectively, all above 4 points. The course was generally well-received by students, and they expressed a strong willingness to continue learning under this teaching model.
Copyright (c) 2026 Wenjing Zhou https://creativecommons.org/licenses/by/4.0/
Research on Security of Distributed Storage System Based on Blockchain Technology
The application of blockchain technology brings more possibilities for the improvement of system security. In this paper, blockchain technology is utilized to strengthen the security of distributed storage system. A multi-tier distributed storage system is constructed by designing the block structure of the transaction blockchain and realizing the two-tier architecture of data management and content storage. All-or-nothing transformation (AONT) method of corrective deletion code is introduced to realize file encryption slicing and data integrity verification to control access rights. Performance validation shows that the write latency of the distributed storage system based on blockchain technology is below 3000ms, and the read response is <30ms. Only 10 out of 1500 sets of data were successfully tampered with. The replica corruption rate is less than 0.2, and the maximum data block corruption rate is not more than 0.056. The node vulnerability expectation is in the range of [0.021,0.431] for the replica corruption rate of the system at node vulnerability expectation of 15-95, and the node load rate fluctuates less than 0.005.
A Study on the Application of Computer-Assisted Sentiment Analysis Methods of Literary Works in Literary and Cultural Education
This paper addresses the need for emotional analysis of literary works by constructing a customized Chinese novel dataset and proposing a progressive learning framework called PL-ERC. This framework generates pseudo-sentence labels through dialogue-level annotation, combining noise density segmentation strategies with LSTM temporal modeling to achieve fine-grained emotional transfer learning. Cross-domain experiments show that the average F1 score reaches 88.87% in a 10-shot task across seven categories of literary works, outperforming the best baseline ConVEx by 2.9%. Ablation experiments reveal that removing the self-training strategy results in the largest F1 drop of 17.72%, while replacing LSTM with RNN reduces F1 by 12.88%. In terms of sentiment classification accuracy, the accuracy rate on the DMSC seven-classification dataset was 97.17%, with an identification rate of over 98% for the anger/happiness categories. Educational evidence shows that the weighted total score of the experimental class applying this method was 94.25 ± 3.92, significantly better than the control class (84.00 ± 7.89, p < 0.01). Students' critical thinking ability scores were 4.67 (control class: 3.51), and aesthetic experience ability scores were 4.72 (control class: 3.74), confirming that technology-enabled education significantly enhances the effectiveness of literary and cultural education.
Copyright (c) 2026 Ye Wang https://creativecommons.org/licenses/by/4.0/
Research on Intelligent Development of Talent Cultivation Mode of Higher Vocational Innovation and Entrepreneurship Education Based on Data Mining under the Perspective of Industry-Teaching Integration
Against the backdrop of the integration of industry and education, vocational college innovation and entrepreneurship education has entered a new phase characterized by diversification, emphasis on intrinsic development, and intelligent development. This article constructs a student learning behavior analysis model, collects student learning behavior data, and proposes the use of the k-means algorithm to analyze student behavior. Additionally, course access, video viewing, assignment performance, daily performance, and interaction are selected as online learning behavior characteristics. The results reveal that vocational college students exhibit a high proportion of passive learners, with relatively low proportions of passive and active learners. Passive learners account for the largest proportion at 87.5%, and learning quality is primarily influenced by factors such as teacher attention, chapter quizzes, assignments, and course credits. The construction of a vocational college innovation and entrepreneurship education talent cultivation model can be approached from three aspects: establishing a comprehensive innovation and entrepreneurship talent cultivation system, implementing an apprenticeship system for innovation and entrepreneurship talent cultivation, and developing an innovation and entrepreneurship course system.
Copyright (c) 2026 Yinxing Zhou https://creativecommons.org/licenses/by/4.0/
A Strategic Study on the Enhancement of the Effect of Data Analysis Technology on Student Management and Civic and Political Education in Colleges and Universities from an Interdisciplinary Perspective
Data analysis technology brings new historical opportunities for the innovative development of student management work in colleges and universities in the new era. This paper relies on the K-prototypes clustering algorithm to construct a student portrait method that serves the precise work of ideological and political work, proposes the index system and modeling method of student portrait, and carries out the clustering division of students in a university. Based on the results of student portrait, we discuss the optimization strategy of Civic Education, construct the Civic Education Effect Evaluation Model, and evaluate the effect of Civic Education before and after the experiment. The study divided the sample students into six categories: "weak awareness", "leading progress", "exemplary leader", "silent effort", "innovative practice" and "poor discipline", among which "guiding progress" and "silent effort" had the largest number of students, accounting for 36.7% and 23.3% respectively. After the implementation of the optimization strategy of the ideological education, the effect of the ideological education was improved from the average level to the better level, and the overall score was improved by 10.39%, which confirms the improvement effect of the student portrait and optimization strategy based on cluster analysis on the ideological education. Data analysis technology can help the development of ideological education in colleges and universities and improve the quality and efficiency of student management through the collection, organization and analysis of student-related data.
Copyright (c) 2026 Wei Luo https://creativecommons.org/licenses/by/4.0/
An Econometric Model of Interest Rate Marketization and Diversification Driving the Performance Improvement of Listed Commercial Banks in China
Commercial banks play a crucial role in China's financial markets, and their stability and health are particularly important for the market and national economic development. The study collected a total of 225 balanced panel data sets from 2015 to 2021 as empirical samples, employing both qualitative and quantitative analysis methods. Focusing on the perspective of diversified operations, the study utilized a econometric model based on panel regression to thoroughly examine the relationship between interest rate liberalization and bank operational performance, as well as the moderating effect of diversified operations. The empirical findings indicate that there is a negative relationship between commercial bank performance and the degree of interest rate liberalization, and that diversified operations mitigate this negative impact. Based on these findings, policy recommendations can be proposed from both the bank and government perspectives to improve the quality of diversified operations.
Copyright (c) 2026 Jin Zhu https://creativecommons.org/licenses/by/4.0/
Principal Component Analysis Methods and Teaching Reform Paths in Precision Teaching of Vocal Music Education in Colleges and Universities
In the exploration of the precise teaching reform of college vocal music, this paper takes the influencing factors of teachers as the research entry point. Aiming at the problems of the principal component analysis network, such as the inability to reflect the complete structural information of the data and the sensitivity to noise, the two-way two-dimensional principal component analysis is used as a filter, and the robust and non-greedy two-way two-dimensional principal component analysis network based on the L1-paradigm distance metric is proposed, and then a principal component analysis network model is constructed. Meanwhile, in order to improve the robustness of principal component analysis, the MCD algorithm, which calculates the robust covariance matrix, is added to the model. Combined with the actual situation, a set of teacher influence factor indicator system containing 7 secondary indicators and 27 tertiary indicators is constructed from the three aspects of teachers' basic situation, teachers' classroom performance and teaching management. Applying the principal component analysis network model to the index system of teachers' influencing factors, the cumulative contribution rate of the six extracted index factors (teachers' situation, teaching attitude, teaching methods and means, teaching organization, system management, process management) has reached 83.61%, indicating that the six aspects are the main entry point for the precise reform of vocal music teaching.
Copyright (c) 2026 Xiaoling Huang https://creativecommons.org/licenses/by/4.0/
Analysis of the Relationship between College Students' Mental Health Fluctuations and The Effect of the Civics Program Based on the ARFIMA Model
This study establishes a methodological framework for quantifying the dynamic relationship between fluctuations in college students' mental health and the effects of ideological and political education courses based on the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. By integrating multi-source heterogeneous data and combining time series feature representation techniques (Fourier transform, piecewise aggregation approximation), the study systematically analyzes the long-term memory characteristics of mental health. Experiments on public datasets (StudentLife, WeiBo) demonstrate that the ARFIMA model significantly outperforms baseline models in mental health level prediction, achieving a weighted average F1 score of 50.56% (an improvement of 7.27% over the best baseline SOTA), with a maximum F1 score of 97.05% on the WeiBo dataset. To address the issue of overfitting in small samples, a low-rank adaptive (LoRA) optimization mechanism was introduced, reducing the validation set loss from 1.051 to 0.956 and enhancing the model's generalization capability. After LoRA optimization, the emotion recognition model demonstrated high discriminative power for positive emotions (97.77% accuracy) and sadness (91.79%), but neutral emotion recognition exhibited confusion (90.12% accuracy). Related analyses of the effectiveness of ideological and political education courses showed that course learning outcomes were most strongly correlated with positive psychological qualities (r = 0.494), particularly in the dimensions of cognition (r = 0.481) and self-control (r = 0.479). Regression analysis further indicates that course content (β = 0.136, p < 0.05) and diverse development (β = 0.089, p < 0.05) are core predictive factors for cognitive qualities. This study provides data-driven decision support for optimizing ideological and political education courses and confirms the effectiveness of time series modeling in mental health interventions.
Copyright (c) 2026 Yiqian Wang https://creativecommons.org/licenses/by/4.0/
Bayesian Network Model-Based Assessment of the Role of Music Intervention in Volleyball Players' Physical Recovery
This paper proposes to explore the mechanism of music therapy's influence on volleyball players' physical recovery under the acquisition of music therapy's extensive mechanism of action. Relevant factors affecting volleyball players' physical recovery are pointed out, including physics, biology, training and psychology. Analyze the composition of Bayesian network and the analysis process, and establish the Bayesian network model of volleyball players' physical recovery. Analyze the role of each influencing factor on the physical recovery of volleyball players, and get the size of the mechanism of music therapy in the physical recovery of volleyball players. Functional music for sports was selected to analyze the effect of music therapy on special intervention for volleyball players. The two-factor repeated measures variance was used to analyze the physical fatigue test data of the experimental group and the control group, and the results showed that the music regulation method has a significant effect on relieving volleyball players' fatigue. Music therapy can act on volleyball players by regulating psychological states, as well as positively affecting them through the body's biological response to vibrating music.
Copyright (c) 2026 Dan Lv, Li Zhou, Wei Li, Yan Wang https://creativecommons.org/licenses/by/4.0/
The Digital Transformation Path of Cross-Border E-Commerce cross-Cultural Communication on the “Digital Silk Road” under the Perspective of Enterprise Operation Synergy
This paper first introduces the basic characteristics of networks and the evolutionary process of complex network models. Building upon complex social networks and traditional SIR models, it introduces two key factors—content homogeneity and dynamic change rates—to propose a dynamic information propagation model with a content homogeneity mechanism, analyzing approaches to addressing information content homogeneity. Through comparative experiments between scale-free networks and small-world networks, it explores the propagation mechanisms of homogeneous information in cross-border cultural networks. The results indicate that the number of edges, average path length, and clustering coefficient in cross-border cultural transmission networks decrease as the threshold increases. Through experiments, the threshold, average path length, and clustering coefficient of cross-border cultural transmission e-commerce networks were ultimately determined to be 0.5, 2.59039, and 0.42242, respectively. The effectiveness of cross-border cultural transmission marketing is enhanced by increases in market size, infection rate, and information survival time, while recovery rate and the number of competitors significantly weaken the effectiveness of cross-border cultural communication marketing.
Copyright (c) 2026 Yan Zhuang https://creativecommons.org/licenses/by/4.0/
Market Development and Economic Benefit Analysis of Intangible Cultural Heritage Cultural and Creative Products Empowered by AI
The rise of generative artificial intelligence technology provides a new path for the design innovation and market expansion of non-heritage cultural and creative products. This paper takes AI-enabled non-heritage cultural and creative products as the research object, and systematically explores its market development characteristics and economic benefit enhancement mechanism. Based on generative adversarial network and residual network, the intelligent optimization framework of non-heritage cultural and creative product design is constructed. Through user demand research, perceptual imagery analysis and controlled experiments, the effectiveness of AI technology in improving design efficiency and product quality is verified. Combining the algorithm test and economic benefit assessment of four types of cultural and creative products, namely hand puppet, earrings, fan and book, to reveal the driving effect of AI-enabled on the market competitiveness and economic returns of non-heritage cultural and creative products. After adopting AI-assisted, the economic benefits of each product are significantly improved, and the economic benefits of the hand puppet and earrings show a growing trend, in which the final benefit of the hand puppet reaches 1,312 yuan/d, and that of the earrings reaches 1,118 yuan/d. Adopting AI-assisted styling design of the cultural and creative products is feasible, which not only improves the design effect of the cultural and creative products but also enhances the economic benefits by doing so.
Copyright (c) 2026 Rongxiang Huo https://creativecommons.org/licenses/by/4.0/
Research on Support Vector Regression-based Profit Forecasting Model for Enterprise Economic Analysis
Profit forecasting based on corporate economics serves as an important indicator of market returns and plays a pivotal role in market investment. This paper randomly selects financial statement data from 3,248 A-share listed companies in the H database for the years 2006–2022 as the research data source, determines the model indicator variables, and performs descriptive statistics on the sample. In constructing the prediction model, to address the limitations of traditional Support Vector Regression (SVR) models in handling large-scale data, the Whale Optimization Algorithm (WOA) was employed to optimize the penalty factor and kernel function parameters within SVR, thereby proposing an SVR-WOA-based profit prediction model. After 50 iterations, the model demonstrated a mean squared error as low as 0.075 on the validation set, exhibiting superior predictive capabilities.
Copyright (c) 2026 Yuan Wang https://creativecommons.org/licenses/by/4.0/
Computational Jurisprudence Approach to the Analysis of Legal Liability for the Protection of Intellectual Property Rights in the Creation of Virtual Reality Content
Based on the computational jurisprudence method, this study gradually completes the scrutiny of the use of computational jurisprudence method from constructing the computational jurisprudence problem, obtaining the legal data in the creation of virtual reality content, selecting the appropriate data analysis model, and then testing and evaluating the model results. And the research idea based on the legal responsibility of intellectual property protection in virtual reality content creation was designed, and for the practical results, the steps to verify the correctness of the results were designed. The results of the research are as follows: when the length of the legal text of intellectual property protection is 600, the model of this paper has the best analyzing effect, and the accuracy rate, precision rate, checking rate, and F1 score are 0.870, 0.887, 0.831, and 0.858, respectively. The score values of the knowledge patent leakage risk indicators "Data Confirmation and Provision Risk", "Data Transmission and Reception Risk", and "Data Storage and Use Risk" are 3.40, 3.35, and 3.51 respectively, and the overall leakage risk level is level 4. The digital copyright attribution boundary score for virtual reality content creation is 2.73, which means that the determination of intellectual property attribution is somewhat controversial. Both computational jurisprudence methods and national economic levels can improve the level of intellectual property protection in virtual reality content creation.
Research on the Construction and Optimization Strategy of Quantitative Assessment Model for Volleyball Players' Physical Training Effect
This article addresses the current lack of accurate and systematic physical assessment methods for volleyball players. A comprehensive assessment method was developed based on Markov models and functional movement screening (FMS). This method quantifies athletes' basic movement abilities through a state transition matrix and seven standardized movement tests. Test results indicate that athletes assessed using this method have shown significant improvements in vertical jump, 30m sprint, and VO2max compared to previous assessments. Additionally, their trunk core stability and body coordination and balance abilities have also improved. Furthermore, after adjustments, the lower limb symmetry training program has been shown to reduce athletes' injury rates. During the study, it was found that with the assistance of this model, changes in athletes' physical fitness can be tracked, providing the basis for developing scientific and systematic training plans. The assessment model proposed in this paper is specifically applicable to volleyball and can serve as a valuable reference for optimizing physical fitness training assessment and scientific systems in other sports. This paper proposes a new approach to scientifically and systematically assess volleyball physical fitness training, thereby optimizing and enhancing the overall competitive ability of volleyball athletes.
Copyright (c) 2026 Dan Lv, Jiale Qu https://creativecommons.org/licenses/by/4.0/
Research on the Teaching Reform Path of Civic and Political Education in Financial Management Professional Course Based on Structural Equation Modeling
The concept of “course-based ideological and political education” represents a new requirement for university teachers in the contemporary era. By integrating this concept into professional courses, the latent educational functions of financial management courses can be fully leveraged to help students establish correct value systems, thereby achieving the unity of ‘teaching’ and “educating” through subtle course instruction. A questionnaire was designed to measure the factors influencing the effectiveness of ideological and political education in financial management courses. It was distributed and collected among college students at a certain college in City S. After conducting factor analysis on the questionnaire, structural equation modeling was applied to explore the factors influencing the development of financial management courses. The results showed that course implementation, teacher-related factors, student-related factors, and teacher-student interaction all have a positive impact on ideological and political education outcomes, with path coefficients of 0.168, 0.529, 0.188, and 0.168, respectively. Therefore, financial management faculty members in higher education institutions should actively engage in course-based ideological and political education reforms to enhance students' learning interest and classroom participation, thereby improving the effectiveness of course-based ideological and political education.
Copyright (c) 2026 Li Zou, Pansheng Luo https://creativecommons.org/licenses/by/4.0/
A Study of Music Composition Models Combining Generative Adversarial Networks and Their Application to Film Scoring
Automatic generation of movie music is one of the current research hotspots in the field of artificial intelligence. This study proposes a music composition model based on the application of artificial intelligence in movie soundtrack composition. The model is based on generative adversarial network and its derivative model CT-GAN, and builds the MTC-GAN music generation method through multi-track correlation modeling, temporal structure modeling and discretization processing. In the objective index comparison experiments, the experimental indexes verify that the MTC-GAN model in this paper can create high-quality music, and its Scale Consistency, Tone Span, and Uniqueness structures are 89.13%, 13.84, and 64.43, respectively, which effectively improves the quality of music generated by the music composition model. The model-generated movie soundtrack score has an overall improvement of 1.48% over the human work score, and its evaluation scores in terms of melody, rhythm and emotion are optimal among all compared models. Experiments show that the method in this paper achieves ideal results in generating music quality, which is helpful to help movie soundtrack creation and promote the innovation and development of movie soundtrack art.
Copyright (c) 2026 Jiangli Jia, Juan Chen https://creativecommons.org/licenses/by/4.0/
Converter Steelmaking Clean Steel Production Process Cost-Benefit Analysis and Key Process Link Dynamic Regulation Strategy
Based on the clean steel production process flow of converter steelmaking, this paper proposes a design method for process flow optimization, utilizing data analysis methods and numerical simulation for analysis and calculation. When applying this scheme, a process data analysis platform for converter steelmaking was created using machine learning and numerical simulation methods to identify the characteristics of process patterns from large-scale production data analysis. The newly proposed dynamic control scheme strategy is based on a model predictive control method, which combines the implementation of important process data information with process feedback, ultimately achieving dynamic adjustment of process parameters to enhance production efficiency. The results obtained from multiple experiments using this scheme and dynamic control methods ensure that the production cost of clean steel is reduced by 8.5% to 15.2% compared to when it is not used, and the production pass rate averages over 97%.During numerical simulation experiments, it was found that there is a relationship between the pressure of top-blown oxygen and the angle of the nozzle, and this relationship has a significant impact on molten steel temperature and decarburization degree. By comparing the production efficiency and energy consumption under different process parameter combinations, it was discovered that the use of a segmented dynamic control method not only improves various metallurgical parameters but also effectively reduces production fluctuations. The numerical simulation experiments demonstrated that this dynamic control scheme has high feasibility and strong practicality, providing an excellent solution for the production process of clean steel in converter steelmaking, with significant potential for widespread application.
Copyright (c) 2026 Lixing Ma https://creativecommons.org/licenses/by/4.0/
Research on the Integration Application of Red Culture in Civic and Political Education in Colleges and Universities Based on Multimodal Data Analysis Algorithm
Utilizing digital intelligence technology to analyze and process multimodal data to promote the deep integration of red culture and ideological and political education in higher education is an important direction for the current educational reform. This paper constructs a red cultural resource value extraction framework supported by large language model technology, comprising four main modules: red resource integration, knowledge graph construction, knowledge reasoning, and intelligent applications. On the knowledge graph of red cultural resources, a BERT+CRF model is constructed for entity extraction to assist in obtaining graph triples. For personalized learning path resource recommendations, a deep reinforcement learning recommendation algorithm optimized for recommendation diversity is introduced, and its decision-making model is designed. Combining personalized learning path intelligent recommendation theory with the extracted red resources, the framework achieves the recommendation of red cultural resources in college ideological and political education courses. The teaching platform equipped with the recommendation system developed in this paper has received over 80.00% positive evaluations from students regarding its effectiveness and over 85.00% positive evaluations regarding satisfaction in actual applications.
Copyright (c) 2026 Wei Wei https://creativecommons.org/licenses/by/4.0/
Personalized Optimization Strategies for Physical Education Dance Training and Teaching in Modern Colleges and Universities Based on Multivariate Data Analysis
With the rapid development of economy and science and technology, it leads to the traditional sports dance training and teaching mode is hardly suitable for students' learning needs. Based on the teaching dilemma escribed above, a personalized optimization strategy for sports dance training and teaching in modern colleges and universities under the perspective of multivariate data analysis is proposed. In order to confirm the practical application value of the optimization strategy, the assessment scheme of the personalized optimization strategy of sports dance training and teaching in colleges and universities is designed under the joint effect of hierarchical analysis, entropy weight method and fuzzy comprehensive evaluation method, and the scheme is analyzed by examples. The evaluation value of the strategy is calculated as (0.7666, 0.1694, 0.1243, 0.0743, 0.0815), and due to obeying the principle of maximum affiliation, it is further concluded that the evaluation result of the strategy is excellent, which sufficiently proves the application efficacy of the strategy.
Copyright (c) 2026 Analysis Jie Jiao https://creativecommons.org/licenses/by/4.0/
Improvement of Clothing Structure Design and Wearing Comfort Based on Jacobi Matrix Optimization Algorithm
Clothing structure design is a key link in the clothing manufacturing process, and the traditional design methods rely on the experience and skills of designers. With the development and application of digital technology in the apparel industry, it has greatly promoted the innovation and development of apparel manufacturing. In this paper, we combine the depth estimation and vision controller methods to construct an image Jacobi matrix to realize the control of the hand-eye mapping relationship of the robot's visual servo, which represents the mapping relationship between the robot's joint speed to the end speed. Using Kalman filtering algorithm, the image Jacobi matrix to be estimated is used as the system state to estimate the system state, so as to achieve the control of the stitch and displacement of the garment sewing, and, at the same time, capture the visual information contained in the garment to optimize the design of the garment structure. For the optimized designed garments using the Jacobi matrix, 4# has the highest mean comfort rating of 4.5 and above. The mean value of satisfaction evaluation for ease of movement and overall comfort of the optimized garment went up to 4. It is evident that the overall comfort of the garment optimized by the image Jacobi matrix algorithm has been significantly improved.
Copyright (c) 2026 Linqian Ma https://creativecommons.org/licenses/by/4.0/
Multi-Dimensional Analysis of the Nurturing Function of Red Culture into the Path of Ideological Education in Colleges and Universities Using Big Data
This paper utilizes distributed computing frameworks and other data processing technologies to achieve intelligent processing of multimodal red cultural resources, providing educational resources for ideological and political education in higher education institutions. By employing a pre-trained BERT model to generate document-word co-occurrence maps, combined with continuous multivariate probability (Dirichlet) distributions to optimize topic representations, and based on an improved BERTopic algorithm, a red cultural topic clustering model is constructed. The results show that when red culture themes are clustered into four categories, the top three keywords in terms of frequency are: ideals and beliefs, patriotic sentiment, and red gene. When the themes are simplified into three categories, the algorithm clustering time is 60.513 seconds, the contour coefficient is 0.264, the CH index is 1267.453, and the content coverage rate is close to 100%. Using the clustered red culture for ideological and political education, students' ideological and political literacy improved by an average of 0.988 points.
Copyright (c) 2026 Lisi Wei https://creativecommons.org/licenses/by/4.0/
Analysis of the Correlation between Teachers' Digital Competence Construction and Basic Education Quality Improvement Based on Adaptive Algorithm
The digital transformation of education is an important strategic choice for building a high-quality development education system in the new era, and the digital transformation of basic education is inevitable in line with the development of the times. This paper explores the impact of teachers' digital competence on the quality of basic education from the dimension of teachers' competence. Teachers' digital teaching ability is divided into four dimensions: teaching design ability, teaching organization and implementation ability, teaching management and evaluation ability, and teaching development ability, and the level of teachers' digital teaching development is investigated. By establishing an adaptive multiple linear regression model, the correlation analysis between the development level of teachers' digital teaching ability and the quality of basic education, and the linear regression analysis, the relationship between teachers' digital teaching ability and the quality of basic education was obtained. The model R2 value of the adaptive multiple linear regression model is 0.833, and the model passes the F test. It indicates that at least one of digital instructional design competencies, organizational and implementation competencies, management and evaluation competencies, developmental competencies, and digital pedagogical literacy will have an impact on the quality of basic education.
Copyright (c) 2026 Xianggui Jing https://creativecommons.org/licenses/by/4.0/
Combining AIGC and Virtual Simulation Technology to Enhance Personalization of Jewelry Design
Empowered by emerging technologies such as 5G, big data, cloud computing, artificial intelligence is developing rapidly, and its application has received widespread attention from various industries. Empowering design, such as jewelry design, through AIGC can improve efficiency, expand creativity, and promote the innovative development of the jewelry design profession. The study firstly imports the data into 3DMax software to complete the construction of the jewelry 3D model, and in the process of model construction, Photoshop and Lidar point cloud computing are used to control the model's color matching and height distance. Based on the genetic algorithm, an interactive genetic algorithm based on user preference model is proposed. After adding the group preference information, the preference dominance relationship of the current user changes only slightly, indicating that this algorithm still focuses on user preference, which is conducive to the rapid tracking of user preference when it changes. Through the method of user research, the KANO model is used to grade the attributes of functional requirements, and design innovations are proposed.
Copyright (c) 2026 Tingting Ma, Yodkwan Sawadee https://creativecommons.org/licenses/by/4.0/
Optimization Study of Greening and Ecological Effect in Public Space Based on Big Data Analysis
People are paying increasing attention to urban environmental quality issues. Future urban competition will not be about speed, height, or scale; instead, concepts such as green, ecological, low-carbon, and sustainable development will be the themes of future urban development. This article analyzes the ecological effects of public green spaces, examines the mechanisms of green space elements, and proposes strategies for creating green public spaces. Taking the public space greening environment of the Pearl River Delta as the research object, this study constructs a public space greening information extraction model based on an improved SRResNet network and a global-local cross-attention mechanism. The model's effectiveness is validated through a dataset, and the optimization effects, ecological effects, and green view rate satisfaction of public space green spaces are analyzed using the SD method and correlation analysis. The results show that the overall accuracy and Kappa coefficient of this model are 93.75% and 0.92, respectively, which are superior to those of the comparison algorithms. The average SD score for public space greening in the Pearl River Delta is 2.47, indicating that the overall ecological effects of public space greening are good, and a green visibility rate of over 29% can significantly improve residents' satisfaction.
Copyright (c) 2026 Liang Wei https://creativecommons.org/licenses/by/4.0/
A Melody Generation Algorithm for Popular Music Based on Multidimensional Spectral Analysis and Its Innovation on Compositional Patterns
In this paper, by performing Fourier transform on the original signal of the musical melody, obtaining its audio signal, and arranging the result to form a speech spectrogram, and utilizing multidimensional spectral analysis method for this to extract the musical melody. On this basis, a convolutional neural network model is added to extract the musical melody by means of the temporal harmonic map convolutional network model. After the pre-training of BERT model, the new musical melody is generated by Transformer, which defines the audio symbols and assists in the musical arrangement. The total number of segments above 1 point in the music clips generated using the model of this paper are 1349, 1047, and 687, respectively. More than 90% of the passages were audibly acceptable, indicating that the structure of the melodic flow generated in this paper has a high degree of similarity to the musical chords, and is subjectively recognized by the listener. The violin audio and the flute audio reached their maximum values in the first and second frames respectively, with the highest frequencies of 0.465 and 0.0198, respectively, and the violin timbre was richer.
Copyright (c) 2026 Hongxu Kang https://creativecommons.org/licenses/by/4.0/
Research on the Influence Mechanism of Naadam Culture on the Physical and Mental Health Development of Adolescents in the Ethnic Areas of the Northern Frontier
Nadam culture is an important component of the overall cultural system of the ethnic regions in Northern Xinjiang, boasting a rich traditional heritage and profound cultural significance. This study focuses on the impact of Nadam culture on the physical and mental health development of adolescents, employing research methods such as experimental design, questionnaire surveys, and mathematical statistics to conduct educational experiments on students at a certain school. Before and after the experiment, the physical health indicators and psychological health factors of the two groups of students were measured and analyzed. The results showed that the experimental group exhibited significant differences in all physical health indicators (p < 0.05), with the most notable improvements observed in the three indicators of pull-ups, sit-and-reach, and sit-ups, with growth rates of 45.24%, 42.34%, and 22.64%, respectively. The total mental health score of the experimental group decreased by 4.49%, which was higher than the 1.03% decrease in the control group. All factors showed significant differences at the 5% level, and the reduction rate was 1.94% to 5.74% higher than that of the control group. Nadam culture had the most significant improvement on other factors, hostility, and interpersonal relationships among adolescents. The above results indicate that Nadam culture has a positive effect on the physical and mental health development of adolescents.
Copyright (c) 2026 Wurigenmuren, Shaoying Liu, Baigalmaa Chultem, Battsetseg Gonchoo https://creativecommons.org/licenses/by/4.0/
Extraction of Traditional Chinese Patterns for Jewelry Design Innovation Using Computer Vision Technology
The use of traditional Chinese patterns in jewelry design has a long history, in order to realize the innovation of the design method, this paper uses computer vision technology to extract traditional Chinese patterns. After preprocessing the traditional pattern images, the edges of the images are detected by the improved Canny algorithm. Then the features of traditional Chinese patterns are extracted by the cascade fine segmentation algorithm in order to be integrated into modern jewelry design. The image extraction effect of the cascade fine segmentation algorithm in this paper is tested, and it is found that its extraction accuracy is far more than other image segmentation and extraction algorithms. The Chinese traditional pattern jewelry designed by this paper's computer vision technology gains “better” evaluation results among the viewer groups. The method proposed in this paper is feasible for the integration of Chinese traditional patterns into the jewelry design field.
Research on the Logical Mechanism and Development Path of Red Culture in Civic and Political Education in Colleges and Universities under Digital Environment
Red cultural resources contain rich ideological and political education value, and it is of great practical significance to utilize red culture to effectively carry out ideological and political education in colleges and universities. The study explores the logical mechanism of red culture in ideological and political education in colleges and universities through questionnaire surveys of teachers and students in two colleges and universities, using statistical methods, establishing theoretical models, proposing research hypotheses, and conducting correlation analysis and linear regression analysis. The results show that the current integration of red culture into civic and political education has achieved certain results, with good performance in students' interest and recognition, teachers' recognition and teaching methods. Both red culture experience practice and red culture propaganda and education positively affect the integration of red culture into the work of ideological and political education in colleges and universities (p < 0.01), with impact coefficients of 0.516 and 0.409. Therefore, it is necessary to integrate high-quality red cultural resources, build a media matrix of red culture and carry out red culture practice activities in order to promote the development of the integration of red culture and ideological and political education in colleges and universities.
Copyright (c) 2026 Yaxu Lei, Wenli Liang https://creativecommons.org/licenses/by/4.0/
Using Big Data Visualization Technology to Study the Connotation, Logical Mechanism and Path of the Integration of Red Culture into Civic-Political Construction in Colleges and Universities
Big data visualization technology provides a new research perspective and practice path for the integration of red culture into the construction of college ideology. In this paper, the existing red cultural resources are firstly digitally collected and processed, combined with computer and AI technology to realize the repair and reproduction of damaged resources, and then equipped with a digital resource library to complete resource integration and sharing. Then the virtual reality teaching scene modeling of red culture into the construction of college ideology, realize the visualization teaching of red cultural resources, and carry out the teaching design of related knowledge points and start the teaching practice. The average grades of the experimental class and the control class before teaching were 72.80 and 72.91 respectively, with a small gap, and the grades of the experimental class after teaching were 8.8 points higher than those of the control class. The students' recognition of this paper's teaching method in terms of nurturing function, value objective, theoretical interpretation and teaching innovation is between 4.40 and 4.80, with a high degree of overall recognition. Through the fuzzy hierarchical analysis method, the communication effect of red culture in the Civics classroom under the virtual reality visualization technology was obtained as 84.77 points, with better evaluation results. The results of the study, fully proved the effectiveness of the red culture based on visualization technology into the path of Civics construction in colleges and universities.
Copyright (c) 2026 Yunbin Ma https://creativecommons.org/licenses/by/4.0/
Research on the Application of Data Mining Algorithms in the Interdisciplinary Talent Cultivation of Environmental Design Majors in the Context of New Liberal Arts
Based on an analysis of the current state of environmental design education, this article proposes educational objectives for environmental design programs. Drawing on the talent cultivation philosophy advocated by the New Liberal Arts initiative, it establishes strategies for constructing an interdisciplinary teaching system for environmental design programs and designs an interdisciplinary talent cultivation model for the field. To validate the effectiveness of this model, students from the 2023 cohort of an environmental design program at a certain university were selected as research subjects. A semester-long teaching practice was designed to explore the impact of the interdisciplinary talent cultivation model on students' academic performance and interdisciplinary competencies. Additionally, an interdisciplinary teaching quality assessment model for environmental design was constructed using the SLIQ algorithm, laying the foundation for in-depth analysis of data related to interdisciplinary talent cultivation in environmental design. The results showed that the final exam scores of the experimental class (HJ1) reached 86.61 points, an increase of 19.45% compared to the pre-test scores. In contrast, the final exam scores of the control class (HJ2) only increased by 1.29 points compared to the pre-test scores, and there was a significant difference between the two classes (p<0.01). The interdisciplinary literacy of students in the experimental class increased by 13.20% compared to before the experiment, and there was a significant difference between the teaching practices before and after the experiment (p<0.01). Therefore, the interdisciplinary talent cultivation model helps enhance the knowledge mastery level of environmental design students and also helps them improve their interdisciplinary literacy level.
Copyright (c) 2026 Haiyan Wu https://creativecommons.org/licenses/by/4.0/
Basketball Training Movement Pattern Recognition and Personalized Teaching Strategy Design Based on Multidimensional Data Analysis
Basketball is a sport with a broad grassroots following, and its scientific research value has also garnered significant attention. Currently, most basketball training content is based on coaches' experience and intuition when training athletes, lacking unified standards and personalized guidance. To address the issues of disorganized data and unclear objectives during training, this paper proposes a method that integrates a dual-stream convolutional neural network with a long short-term memory network, based on video analysis techniques, motion trajectory tracking, and knowledge graph construction technology. It also employs a diffusion probability model for player synthesis, a Monte Carlo optimization model for shot strategy decision-making, and multi-object tracking technology to resolve the issue of mutual obstruction between players, the ball, and the court during games. Through actual testing, it was found that the model and algorithms constructed in this paper not only improve training effectiveness but also provide personalized, targeted technical improvement suggestions for different individuals. By fully utilizing mechanical analysis and motion trajectory tracking methods, as well as machine learning and computer vision algorithms, this study enhances the scientific nature of basketball training and provides new insights and methods for technical analysis research and training content development in other sports fields.
Copyright (c) 2026 Yu Lei https://creativecommons.org/licenses/by/4.0/
Research on Optimization of Assembly Quality Inspection System of Industrial Robot Based on Machine Vision and Improvement of Enterprise Benefit
In the process of Industry 4.0 and industrial intelligence, industrial machine vision plays a crucial role in the quality inspection of workpiece assembly. It can effectively address production and manufacturing issues caused by workpiece quality problems during assembly, thereby significantly improving production quality and efficiency. This paper optimizes traditional industrial robot assembly quality inspection methods by adopting machine vision technology, combining hardware and algorithms to enhance the machine vision system. The TMS320C6711 digital signal processor serves as the core for image information processing, integrated with the EMIF interface, DMA, and interrupt mechanisms to achieve high-speed data transmission and real-time image processing capabilities. A multi-module collaborative solution based on SAA7111, CPLD, and MCU is adopted to optimize the image acquisition module, further enhancing system stability and flexibility. Additionally, an intelligent lighting optimization method combining PWM control algorithms with neural network scene recognition is employed to effectively address the issue of low assembly quality detection accuracy under varying lighting conditions, ensuring reliable performance in complex lighting environments. Results show that compared to traditional machine vision systems, the newly optimized system offers higher accuracy and classification accuracy, with detection accuracy improving from 92.77% to 94.15%, representing an 11.38% increase. Additionally, compared to traditional detection methods, the system significantly reduces detection time, enhancing the operational efficiency of industrial robots. It provides a reliable basis for assembly quality inspection in industrial robots and offers valuable insights for the application of machine vision in industrial fields.
Copyright (c) 2026 Ang Li https://creativecommons.org/licenses/by/4.0/
Multidimensional Communication and Innovative Path of Red Culture in Civic Education of Colleges and Universities Driven by Artificial Intelligence
Red cultural resources have a long history, rich content and deep heritage, which are rooted in the excellent traditional Chinese culture, innovated in the historical process of Marxism's Chineseization, and formed in various historical periods of revolution, construction and reform. The study proposes the SNIIR communication model in response to the insufficient phenomenon in the SIR model communication process for the effective communication of red culture in ideological and political education in colleges and universities. The data sets of Advogato and Anybeat social network platforms are selected for static topological characterization, and the overall structure of the network and its importance in information dissemination are explored, and the results show that the research results indicate that 46 and 157 in the network will have a high degree of activity in terms of the role of information release and information dissemination, and that they have a stronger ability to guide the dissemination of information in the network. Taking the content dissemination of red culture articles as an example, we get the change of the number of retweets in each hour of the first 24 hours, and optimize the corresponding parameters of the SNIIR model and the traditional SIR model to get the optimal parameters about the fitting of the case event data. On the basis of the case data, we compare and verify the effects of the traditional SIR model and the improved SIR model, and the results of the experimental comparison show that the SNIIR model can better illustrate the general evolution of the topic spreading on the social network, and is more in line with the current information dissemination process of the social network.
Copyright (c) 2026 Dan Li, Pei Zhao https://creativecommons.org/licenses/by/4.0/
Red Culture Enabling Civic and Political Education in Colleges and Universities in a Digitalized Environment: Exploring the Path of Benefit Enhancement under the Perspective of Enterprise Operation
This study is based on the digital environment and explores ways to enhance the effectiveness of red culture in college ideological and political education from the perspective of corporate operational efficiency. A three-dimensional framework for integrating red culture into higher education ideological and political education in the era of integrated media has been systematically constructed. To uncover the intrinsic mechanisms of red culture dissemination, this study proposes a research hypothesis based on communication theory: media interest (PB) and media perception frequency (QD) positively influence cognition (BU) and attitude (XW), which ultimately impact cultural identity (ID). A questionnaire incorporating these five dimensions was developed accordingly. Structural equation modeling (SEM) was employed for empirical validation. Based on empirical findings and corporate operational logic, the study proposes four pathways for enhancing benefits in the integration of core values, mutual learning of management methods, collaborative practice, and co-construction of campus culture between ideological and political education and corporate culture. Descriptive statistics show that students have high levels of cognition (e.g., BU3 = 4.51) and identity (e.g., ID2 = 4.22) toward red culture, but there are differences in media perception frequency, particularly in meeting channels (QD4 = 2.74), which are significantly lower. Confirmatory factor analysis (KMO = 0.876, Bartlett's test p < 0.001) and composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) confirm the scale's good validity. After model correction (removing the insignificant QD4 path) in SEM analysis, the model fit was ideal (CMIN/DF = 1.347, RMSEA = 0.035, GFI > 0.9). The study indicates that course instruction (PB3) and campus activities (QD5) are the core communication channels.
Copyright (c) 2026 Yuyao Wu https://creativecommons.org/licenses/by/4.0/
A Study on Automated Recognition of Grammatical Errors in English Language Acquisition Based on BERT Modeling
Due to the internationalization of English, many non-native English learners have developed incorrect pragmatic habits. Traditional manual correction of English pragmatic habits is both inefficient and difficult to adapt to large-scale teaching. This paper proposes a multi-task learning grammar error recognition framework based on the combination of BERT, BiLSTM, and CRF structures. In multi-task learning, the BERT language model is used for feature extraction of grammar errors, combined with BiLSTM for sequence learning and CRF for global optimal feature extraction. The three-layer structure model trained through multi-task learning can effectively identify various types of grammatical errors, achieving an average F1 score of 88.0% in evaluation metrics. The model demonstrates excellent performance across different types of grammatical error recognition, with high accuracy and robustness. Through multi-task learning, the grammatical error recognition model exhibits strong generalization capabilities, providing intelligent and personalized technical support for English education and marking a new step toward the intelligentization of future educational practices.
Copyright (c) 2026 Dongmei Li https://creativecommons.org/licenses/by/4.0/
Proposing an Optimal Strategy for Modernization Path Design of Vocational Education Governance Based on Multi-Objective Planning Approach
At present, the state urgently promotes the modernization of the governance system and governance capacity, and further improves and perfects the system of modernization of vocational education governance, which has become an important topic that needs to be urgently solved for the reform of the field of vocational education. The article analyzes the logical requirements for the modernization of vocational education governance from the connotation performance and measurement standard of vocational education governance modernization. On this basis, guided by the multi-objective optimization planning theory, a multi-objective planning model for the modernization of vocational education governance is constructed from the development goal of the modernization of vocational education governance. Then based on the GWO algorithm, the GWO algorithm is improved by introducing the chaos mapping strategy and the leading cooperation mechanism strategy, and the IMGWO algorithm is used to solve the multi-objective planning model of modernization of vocational education governance. Finally, the optimal construction path of vocational education governance modernization is proposed from the three dimensions of integration, pioneering and driving. The study shows that the IMGWO algorithm has a better overall improvement effect, has a better optimization accuracy and convergence speed compared with other comparative algorithms, and it is feasible to use it to solve the multi-objective planning model for modernization of vocational education governance. Relying on the multi-planning model of modernization of vocational education governance, it can provide decision-making guidance for the construction path of modernization of vocational education governance and better promote the development of modernization of vocational education governance.
Copyright (c) 2026 Fan Li https://creativecommons.org/licenses/by/4.0/
Research on the Analysis of Thermal Environment and Lighting Balance Efficiency of Green Buildings Based on Optimization Calculation Methods
In the context of growing concerns over global warming and environmental pollution, the construction industry is facing unprecedented energy conservation burdens and environmental pressures. This paper aims to address issues related to indoor thermal environment and daylighting balance in green buildings. By employing optimization algorithms to calculate and design systems, the paper seeks to optimize building performance and explore new strategies for energy balance and daylighting balance in green building design. Using a non-dominant sorting genetic algorithm and parametric design software, an evaluation model for indoor environmental comfort in green building design is constructed, and the effectiveness of the optimized design strategy is validated. This research method begins with an application study of green technology innovations on building energy conservation and emissions reduction effects, based on actual surveys and field data collection. Not only did the optimized design control building energy consumption and indoor thermal environmental comfort, resulting in a 19.5% improvement in energy-saving indicators per unit building area and thermal comfort satisfaction in green buildings (from 70% to 85%), but it also made valuable contributions to filling gaps in the field of green building research, providing useful references and bases for the practical application of green buildings. The study demonstrates the significant potential of green buildings for energy conservation, emissions reduction, and protection of the Earth's environment.
Copyright (c) 2026 Hui Xiong, Lushan Shi https://creativecommons.org/licenses/by/4.0/
Analysis of Vocational Education Governance Model Innovation and Its Sustainable Development Path Based on Weighted Clustering Algorithm
The article conducts an in-depth study on vocational education governance and proposes weighted clustering algorithm for vocational education information fusion through clustering criterion and entropy weight method. It further proposes a dynamic weighted clustering algorithm for fusion clustering of vocational education information. Thus, the governance model and sustainable development path of vocational education are explored. The effect of the innovation of vocational education governance model in this paper is obtained through the pre- and post-test data of students' academic conditions, independent sample t-test and evaluation of governance effect. The students' academic conditions in the six dimensions have been improved substantially, and there is a significant difference after the independent sample T-test. The scores of this paper's vocational education governance model and path based on the weighted clustering algorithm on the guideline level indicators (goal planning and policy adaptability, resource guarantee and allocation efficiency, process management and operational effectiveness, result output and quality level, sustainable development and social reputation) are within the interval of [73.258, 75.768], which has a good governance effect.
Research on the Optimization Strategy of English Translation System Based on Fuzzy Control Algorithm and Its Effectiveness in Teaching Application
Addressing the current shortcomings of English machine translation, such as poor accuracy and ambiguity, this paper proposes a research approach based on constructing semantic mapping relationships, determining semantic order, and achieving accurate machine translation. After processing and analyzing the context of natural language using generalized relationships between concepts, fuzzy evaluations are conducted on the objectives between different ontology concepts to obtain the set of symbols with the highest semantic relevance within the domain knowledge, thereby establishing the semantic model for machine English translation. The maximum entropy training algorithm is used to classify the semantics of English machine translation, and the weighted hierarchical analysis method is employed to calculate English semantic similarity based on fuzzy selection rules, thereby achieving semantic reordering in English machine translation. Combining the fuzzy decision tree method, a hierarchical English machine translation model is constructed, and optimization strategies for the English translation system are proposed. The designed translation model demonstrates reliable application performance in assisting college English teaching, with course effectiveness achieving a positive satisfaction rate of 70.00% or higher among students.
Copyright (c) 2026 Qilu Xu https://creativecommons.org/licenses/by/4.0/
A Study on Optimizing the Learning Effectiveness of Copyright Knowledge in Music Copyright Education Using the Random Forest Algorithm
With the continuous development of science and technology, the issue of learning copyright knowledge in music copyright education has become increasingly severe. In response to the problems highlighted above, this paper proposes a study on optimizing the effectiveness of copyright knowledge learning based on the random forest algorithm. By integrating music copyright education issues and three principles for constructing an evaluation system, this study establishes an evaluation indicator system for copyright knowledge learning effectiveness. Under the influence of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the evaluation indicators are quantified. Based on this, the Random Forest algorithm is utilized to optimize the quantified data of each evaluation indicator, followed by an exploration of the optimization effects of the Random Forest algorithm. The optimization values of the four algorithms are 3.9365, 3.7213, 3.7323, and 3.7088, respectively. It can be concluded that the optimization effect of the RF (random forest) algorithm is higher than that of the DT (decision tree algorithm), LSTM (long short-term memory network), and LR (logistic regression), verifying the practical applicability and application value of the research scheme proposed in this paper, which has practical strategic significance for improving music copyright education.
Copyright (c) 2026 Yiran Gao, Professor Dr Siti Zobidah Binti Omar https://creativecommons.org/licenses/by/4.0/
Research on the Integration Path of Administrative Informatization Construction and Intelligent Management Technology in Colleges and Universities under the Strategy of a Strong Education Country
Under the strategy of education power, it is found that the current administrative management level of many colleges and universities fails to meet the standard requirements, for this reason, this paper puts forward the fusion path of administrative informatization construction and intelligent management technology. Through the theoretical analysis of the influencing factors of the integration path, 4 first-level indicators and 20 second-level indicators are identified to form the evaluation index system. Under the role of fuzzy hierarchical analysis method, entropy weight method and fuzzy comprehensive evaluation method, the design work of fusion path evaluation program is finalized. Under the support of research data and program, the evaluation and analysis of the fusion path of this paper is carried out. Through the calculation of the fusion path of this paper, we first get the affiliation degree value (0.4115, 0.0375, 0.0427, 0.0523, 0.0731), and further get the overall assessment result of the fusion path of this paper, 4.0017, which is in the range of 4~5, indicating that the fusion path of this paper has an excellent assessment grade, and it is very good to verify the application effect of the fusion of college administrative informationization construction and intelligent management technology, and make the college administrative informationization construction and intelligent management technology more effective. The application effect of the path makes the administrative management of colleges and universities more in line with the strategy of a strong education country.
Copyright (c) 2026 Lingzhi Meng https://creativecommons.org/licenses/by/4.0/
Intelligent Pathways for the Preservation of Traditional Chinese Music Culture and Optimization of the Education System in the Big Data Environment
With the development of digital technology, the inheritance of traditional Chinese music culture requires the integration of intelligent technology advantages with the characteristics of traditional Chinese music culture, updating the path of inheritance and development, and accelerating the transformation of traditional Chinese music culture education towards intelligence and professionalism. Digital technology enables the intelligentization of traditional Chinese music culture inheritance. In the field of education and teaching, regarding data security management, this paper proposes utilizing blockchain's hash functions and cross-chain technology to design a cross-chain-based copyright protection scheme for traditional Chinese music culture resources, and establish a blockchain-based digital copyright protection and transaction system for traditional Chinese music culture. Functional testing of the blockchain-based digital copyright protection and transaction system for traditional Chinese music culture is conducted to verify its effectiveness in promoting the inheritance and development of traditional Chinese music culture. Surveys of students' perceptions of the inheritance and development of traditional Chinese music culture in school educational activities have identified activities that students recognize as important for inheritance. Combining digital technology, it is proposed that the inheritance of traditional Chinese music culture can be conducted thematically based on educational activities that students enjoy, ensuring the high quality and professionalism of the inheritance of traditional Chinese music culture.
Copyright (c) 2026 Yun Li https://creativecommons.org/licenses/by/4.0/
Design and Optimization of a Weighted Comprehensive Evaluation Model for Civics Teachers' “Six Essentials” Literacy in the Digital Era
The digital age has placed new demands on the qualifications of ideological and political education teachers. In order to explore the level of the “Six Essential Qualities” of ideological and political education teachers, this paper first proposes the use of the Delphi method to determine the evaluation index system for this study based on the theoretical support of relevant definitions and principles First, based on relevant definitions and principles, the Delphi method was proposed to determine the evaluation indicator system for this study. Subsequently, the Analytic Hierarchy Process (AHP) was used to assign weights to each evaluation indicator. Finally, the additive concept was introduced to optimize the traditional weighted evaluation model, making the assessment results more aligned with actual circumstances, thereby achieving the design and optimization of the weighted comprehensive evaluation model. With the help of the model in this paper, the “Six Essential Qualities” of ideological and political teachers in the digital age were evaluated and analyzed. Through the corresponding formula calculation, the overall evaluation score of the evaluation indicators was 4.3013, which can be summarized as the “Six Essential Qualities” of ideological and political teachers in the digital age being rated as excellent, which not only verifies the optimization effect of the model, but also demonstrates the level of the “Six Essential Qualities” of ideological and political teachers.
Copyright (c) 2026 Yuxin Meng https://creativecommons.org/licenses/by/4.0/
Research on the Mechanism of Artificial Intelligence-Driven Government Innovation Services to Enhance Manufacturing Innovation Capabilities
The rapid development of artificial intelligence has injected new momentum into manufacturing enterprises to enhance their independent innovation capabilities and strengthen their competitive advantages. Manufacturing enterprises should actively adapt to this development trend, seize the driving force provided by technological development, and enable Chinese manufacturing to form new advantages in global market competition. Therefore, this paper comprehensively reviews scholars' research on the artificial intelligence transformation of manufacturing and manufacturing innovation capabilities, proposes the theoretical foundation of innovation-driven and endogenous growth, and analyzes the current level of innovation capabilities of manufacturing enterprises under artificial intelligence technology. Using a two-way fixed effects model, we constructed a measurement framework and proposed relevant research hypotheses, designing a basic regression and mechanism testing model to analyze each research hypothesis individually. The findings indicate that the regression coefficient for the impact on the number of R&D personnel is 1.648, which is significantly positive at the 1% level. The application of artificial intelligence can increase the number of R&D personnel in an enterprise and promote employee participation in process innovation, thereby validating Hypothesis 1. Mediation effect tests were conducted simultaneously for industry type, innovation strategy, and government subsidies. When the explained variable was corporate innovation performance, the regression coefficients for industry type, innovation strategy, and government subsidies were 0.186, 0.197, and 0.185, respectively, all of which were significantly positive at the 5% level. This indicates that improvements in industry type, innovation strategy, and government subsidies can positively influence corporate innovation performance, thereby validating Hypotheses 2, 3, and 4.
Copyright (c) 2026 Yundong Liu https://creativecommons.org/licenses/by/4.0/
Intelligent Algorithm Based Personalized Training Plan Generation and Implementation Strategy for Tennis Physical Education
In the era of mobile internet, data-driven precision training represents a significant trend in the evolution of technical training from traditional experience-based instruction to digital intelligent precision training. The application of intelligent algorithms in the design and implementation of personalized training programs for tennis constitutes the application of data-driven precision training enabled by intelligent technology in tennis instruction and training. The study employs intelligent wearable devices and computer vision algorithms during training to collect parameters such as athletes' physiological data and technical movement parameters. Data is analyzed and mined using swarm intelligence optimization algorithms and deep learning training models, enabling dynamic adjustments and the personalized formulation of scientific training plans. Research findings indicate that intelligent training algorithms can enhance training effectiveness, technical movement standardization, and prevent sports injuries. Athletes in the intelligent training group achieved significant improvements in metrics such as serve accuracy, forehand hitting power, and backhand return success rate compared to those in the traditional training group. Additionally, data on heart rate, blood oxygen recovery time, and the incidence of sports injuries showed a significant reduction. The intelligent video game-assisted training unit designed in this study demonstrates unique effectiveness in cultivating athletes' tactical awareness and the coordination of movements across different body parts. In the comparison of tennis training data, extensive training data confirms the effectiveness of intelligent algorithms in tennis teaching and training, providing new directions for the future development of intelligent algorithms in tennis teaching and training, as well as technical training methods.
Copyright (c) 2026 Siqi Mi https://creativecommons.org/licenses/by/4.0/
A Study on the Teaching Effectiveness of Computer Simulation Technology in Assisting Volleyball Tactical Training and the Improvement of Athletes' Cognitive Abilities
This paper applies computer-assisted technology to the field of volleyball instruction, designing a CAI system for volleyball tactical training. Relevant experiments were then designed to evaluate the teaching effectiveness of the CAI-based volleyball tactical training method and its impact on athletes' cognitive abilities. There were no significant differences in basic technical skills and tactical combinations between the experimental group and the control group in the pre-test. After the experiment, significant differences emerged between the two groups in basic technical skills and tactical combinations (P < 0.05). The experimental group scored higher than the control group by 4.01, 2.60, 3.30, 3.60, 3.22, 3.82, 4.42, 3.76, and 4.08 points across nine sub-dimensions. The cognitive abilities of athletes in both the experimental and control groups remained at similar levels in the pre-test. After the experiment, the experimental group outperformed the control group by 6.68, 7.58, 9.76, and 9.20 points in four sub-dimensions of cognitive ability, with a significance level of less than 0.05. This indicates that the CAI-based volleyball tactical training method used in this study has good teaching effects and can significantly improve athletes' cognitive abilities.
Copyright (c) 2026 Fengxia Yue https://creativecommons.org/licenses/by/4.0/
An Algorithmic Study on Improving the Quality of English Writing Teaching to College Students in an Intelligent Environment
In response to the need to improve the quality of English writing instruction in higher education institutions in an intelligent environment, this paper proposes a writing diagnostic system based on Jieba word segmentation and the EVCM model. A teaching model framework oriented toward interactive instruction is constructed, and the Jieba word segmentation algorithm is optimized for text preprocessing. The EVCM model is designed to achieve lexical coherence diagnosis from three aspects: entity distribution, coreference resolution, and conjunction detection. The stability and diagnostic accuracy of the system under different network environments are verified through simulation experiments. Teaching practice activities were conducted, and T-tests and Pearson correlation analysis were used to explore the impact pathways of the system on improving the quality of English writing instruction. Teaching experiments showed that the pre-test mean score for the high-performance group was 8.95, which increased to 9.96 in the post-test. The mean score differences between the pre-test and post-test for the medium- and low-performance groups were both greater than 1.5 (p < 0.001). Students grouped by English writing performance exhibited significant differences in the overall use of the English writing diagnostic system and in four dimensions: self-motivation, monitoring and diagnosis, execution, and result generation. The use of the system not only directly affects students' writing proficiency but also indirectly promotes ability development by enhancing their writing motivation and beliefs.
Copyright (c) 2026 Liyun Xu https://creativecommons.org/licenses/by/4.0/
Intelligent Assessment of Athletes' Physical Fitness Data and Optimization of Physical Education Teaching and Training Strategies in the Framework of Multivariate Statistical Analysis
Athletes' physical fitness evaluation is not only an important part of sports work, but also an important part of school education evaluation system. This study carries out a multivariate statistical analysis between the various scores of athletes' sports test and the total score, establishes the regression equation between the total score and the physical form and function, and carries out an intelligent evaluation of athletes' physical quality through the regression model. At the same time, the factors affecting athletes' physical quality were explored based on the regression equation. After analyzing, athletes' physical quality can be assessed based on Athletes' physical quality=67.632+0.163×T1-0.263×T2+0.063×T3. Athletes' physical quality is mainly affected by individual factors and school factors. In this regard, this paper puts forward the optimization strategy of sports teaching and training from three aspects: cultivation mode, teaching method and evaluation mechanism. This is of great practical significance to improve athletes' health and physical quality.
Copyright (c) 2026 Yefei Zhang, Chunlin Qin https://creativecommons.org/licenses/by/4.0/
Research on Rain Removal Method for High Scale Rain Pattern Image Block Based on Sparse Representation Model
This paper addresses the problem of rain removal in high-rain-texture images by proposing a novel processing method that integrates a sparse representation model. This approach aims to overcome the processing bottlenecks exhibited by traditional image rain removal techniques and deep learning methods in scenarios with dense rain textures. The paper introduces a sparse representation framework with the ability to model rain texture prior features, enabling the model to preserve natural background details in the image while avoiding excessive smoothing caused by the lack of targeted modeling during the processing. By constructing a sparse dictionary specifically tailored to rain texture structures and leveraging their statistical characteristics in both the frequency and spatial domains, the system achieves significantly superior performance in high-rain-texture regions compared to conventional non-structured methods, supported by structured modeling. To enhance the model's adaptability to complex scenes, this paper integrates a Generative Adversarial Network (GAN) with the sparse representation mechanism, enabling the entire rain removal system to simultaneously possess both representational and generative capabilities during image reconstruction. The results show that the proposed algorithm achieves significant performance improvements on the publicly available synthetic datasets Rain100H and Rain200L, outperforming most existing mainstream methods in both PSNR and SSIM. Especially in terms of visualization, the generated images achieve an ideal balance between preserving texture details and reducing rain texture interference, fully demonstrating the practical value and theoretical feasibility of this method in high-density rain texture environments.
Copyright (c) 2026 Kan Ni, Xiongwen Jiang, Qiyu Ni, Seiji Hashimoto https://creativecommons.org/licenses/by/4.0/
Coupling Mechanism of Intelligent Optimization Design of TMD Parameters of Bidirectional Dense Rib Cavity Building Cover with BIM Technology Application in Enterprises and the Path to Improvement of Operational Efficiency
Traditional vibration-resistant methods have limitations and cannot effectively suppress structural vibration, which may jeopardize the safety of building construction and reduce the operational efficiency of building construction. The article combines BIM technology with the optimal design of TMD parameters of two-way dense ribbed cavity building cover, designs a horizontal omnidirectional TMD device, and introduces genetic algorithm to obtain the optimal TMD parameters. After considering the flutter uncertainty, a case study is carried out based on the two-way dense-ribbed cavity building model constructed by BIM technology. The results show that the flutter vibration control efficiency can reach up to 45.15%, and the longitudinal root-mean-square (RMS) vibration reduction rate of HO-TMD is 15.37% while realizing active frequency modulation. In engineering practice, the introduction of BIM technology can help establish an accurate bidirectional dense-ribbed cavity building cover, which makes the optimized design of TMD parameters more targeted and improves the safety of building construction and operational efficiency.
Copyright (c) 2026 Jianyong Chen https://creativecommons.org/licenses/by/4.0/
Research on the Path of Teachers' Professionalism Improvement Based on Principal Component Analysis under the Framework of Basic Theory of Education
Teachers' professional competence levels influence students' learning quality. This paper constructs an evaluation index system for professional competence within the framework of basic educational theory and systematically analyzes the requirements for the development of teachers' professional competence. Principal component analysis (PCA) is used to reduce the dimensionality of the evaluation data. For some non-linear data, kernel principal component analysis (KPCA) is introduced to complete the principal component calculation. By calculating the feature vector load matrix and T² statistic, the principal component factors are extracted, and teachers' professional competence is ranked. The study shows that the sum of the variance contribution rates of the first four principal components is 94.247%. The four principal components are defined as practical professional competence, innovative professional competence, ethical professional competence, and information professional competence. The comprehensive principal component scores of the eight teachers range from [0.015, 0.064]. Based on the principal component analysis results, four pathways for enhancing professional competence were proposed.
Copyright (c) 2026 Wangkai Xu https://creativecommons.org/licenses/by/4.0/
Research on node position estimation method for wireless sensor networks based on artificial neural network
In this paper, classical wireless sensor network self-localization algorithms (e.g., ranging algorithms vs. non-ranging algorithms) and position computation are firstly described. For the challenge of node localization in three-dimensional space, a model of RSSI ranging system in three-dimensional space is constructed, and a position estimation model based on RSSI ranging by the great likelihood method is established on this basis. In order to further improve the accuracy and robustness of the model, a node localization algorithm based on convolutional neural network for wireless sensor networks is innovatively proposed. The results show that: the algorithm in this paper can maintain high localization accuracy under different numbers of working nodes, and the prediction error of the nodes of the model is less than 13% under different heights from 20m to 200m, and the model has a good high degree of adaptability; at the same time, the algorithm effectively reduces the communication and computation overhead of the nodes, and significantly prolongs the survival time of the wireless sensor network.
Copyright (c) 2026 Xinrui Chen https://creativecommons.org/licenses/by/4.0/
Intelligent Clothing Design System Based on AIGC and Virtual Simulation Technology
In this paper, an intelligent clothing design system with characteristic features is developed through virtual reality technology, and the system contains three functional modules, namely, 3D mannequin, personalized design and virtual fitting, and intelligent matching recommendation of clothing styles, to realize the creative clothing design experience. Aiming at the insufficiency of traditional collaborative filtering algorithms, a clothing recommendation algorithm incorporating user preference trust (PCUCF) is proposed, and the proposed algorithm is applied to the system to complete the functional integrity of the system. The clothing features are summarized into four modules: color features, style features, pattern features and decorative features, and the feature importance score and feature preference are calculated, and the 16 best feature sets are summarized in the feature preference part, and the recommendation algorithm has an accuracy rate of 86.1% for the classification of clothing styles, which is significantly improved compared with other models. After human try-on test, the results show that the intelligent clothing design system can track the respiratory state of human body through the change curve of human body temperature, and at the same time meet the comfort requirements.
Copyright (c) 2026 Xiaoxuan Nie, Li’na Zhao https://creativecommons.org/licenses/by/4.0/
Quantitative analysis of business English civic education: construction of an evaluation model based on fuzzy theory
In order to accurately measure the effect of Business English Civic and Political Education, this paper constructs a corresponding evaluation index system. The Fermatean fuzzy set of indicator data is established to calculate the uncertainty of evaluation indicator data. At the same time, through the sorting function and Zhenyuan integral operator, the relationship between the index attributes is judged to improve the accuracy of the evaluation indexes. In the fuzzy evaluation stage, the factor set and factor subset are established, the classification weights are calculated according to the indicator level, and the weight matrix is composed to judge the evaluation results. Hierarchical analysis and weight consistency test are introduced to complete the reasonable adjustment of weights and reduce the quantitative analysis error rate. In the evaluation index system of the effect of business English civic education, the highest weight is political quality, which reaches 0.4781. The weight consistency test is less than 0.1000. The subordination degree of the first and second level of the comprehensive rating of the civic teachers is more than 0.05, which is at a good level. Through fuzzy evaluation, the level of Business English Civics education can be judged intuitively.
Copyright (c) 2026 Menglin Deng, Ken Chen https://creativecommons.org/licenses/by/4.0/
Research on Virtual Costume Design for Traditional Cultural Symbols: An Intangible Heritage Display Method Based on Computer Vision Technology
Intangible heritage carries the historical memory and cultural genes of human beings, and the rise of computer vision technology brings new opportunities for its protection and inheritance. In this paper, we start from the application of computer vision technology to the display of intangible heritage, and introduce the convolutional neural network VGG-19 optimization model into virtual clothing design to construct a clothing image style migration model based on traditional cultural symbols. Experiments show that the model's index results in peak sex-to-noise ratio, structural similarity, root-mean-square error and style migration speed are better than the performance of the comparison methods, and its peak sex-to-noise ratio is improved by 5.09%~51.43%, which proves the high quality and high integration effect of the style migration images generated by the model. When using images containing different traditional cultural symbols for apparel image generation, the non-heritage cultural images with better migration effects in terms of texture color, artistic rendering effect, and creativity of the apparel are the celadon style, the New Year's painting style, and the Dunhuang mural style, with the support rate of the respondents being 49%, 35%, and 39%, respectively. The proposed series of methods can assist the design of clothing styles and broaden the application scope of non-heritage traditional cultural symbols.
Research on Image Recognition Technology to Assess the Quality of Badminton Players' Stroke Action and Training Improvement Strategy
This paper establishes a motion agent system based on image recognition to assess the quality of badminton players' stroke techniques, and provides personalized training improvement plans using deep learning and biomechanical analysis. Infrared dot high-speed cameras are used to capture the movements of the athlete's shoulder joint, elbow joint, and wrist joint, among other positions. Convolutional neural networks and long short-term memory networks are employed to classify and evaluate the movements of forehand strokes, pushes, and hooks. The action recognition accuracy rate and feature point detection error rate are 96.2% and 93.2%, respectively. The athletes' technical consistency, movement standardization, and training effectiveness improved by an average of over 30% compared to pre-training levels. This paper effectively overcomes the subjectivity and errors of traditional evaluation methods, providing a quantitative, scientific, and accurate method for evaluating badminton technical movements. This study has practical application value and provides theoretical and methodological guidance for the use of image recognition in research on other sports.
Copyright (c) 2026 Rong Pang, Xiaoniu Jiang https://creativecommons.org/licenses/by/4.0/
Accuracy Improvement of Word Vector-Based Machine Translation Algorithm in the Communication of National Community Awareness
In this paper, word vector alignment is introduced into the machine translation model to initialize the word embedding layer of the neural machine translation model for joint training. A monolingual corpus is used to train the model for denoising and self-encoding to improve its encoding ability, decoding ability and translation accuracy. The People's Daily is chosen as the data source, and the study shows that interactive metadiscourse is more frequently used in the news discourse of People's Daily, with 332,433 words, accounting for 81.61% of the total number of metadiscourse, so as to enhance the effect of the communication of national community consciousness. Through the questionnaire survey, it is found that the national community consciousness enhancement score of college students is 4.69, and through the method of this paper, it can provide intelligent solutions for the dissemination of the national community consciousness, which greatly improves the dissemination effect of the excellent traditional Chinese culture.
Copyright (c) 2026 Mengjia Peng https://creativecommons.org/licenses/by/4.0/
Optimization of the Development Path of Innovation and Entrepreneurship Education in Applied Universities Based on the Solution of Nonlinear Equations
This paper applies nonlinear equations to higher education innovation and entrepreneurship education, aiming to identify the factors influencing the effectiveness of such education and to identify pathways for optimizing it. After completing the research design, the author constructed a nonlinear regression model for the effectiveness of higher education innovation and entrepreneurship education using support vector machine regression and a multi-layer perceptron. The research hypotheses were tested through empirical research. The correlation coefficients between teaching environment, classroom interaction, student performance, teacher performance, teaching system support, and the effectiveness of innovation and entrepreneurship education; between teacher performance and student performance; between teaching system support and teaching environment; and between teaching system support and classroom interaction are all greater than 0.7 and significant at the 0.01 level. Teaching environment, classroom interaction, student performance, teacher performance, and teaching system support positively influence the effectiveness of innovation and entrepreneurship education in higher education institutions. Teaching system support positively influences teaching environment and classroom interaction, and teacher performance positively influences student performance.
Copyright (c) 2026 Xue Kaili, Simon Kwong Choong Mun https://creativecommons.org/licenses/by/4.0/
Enhancement of Innovativeness of Dance Works by Artificial Intelligence-Based Dance Creation System
The gradual development and maturation of artificial intelligence technology has driven its deep integration and application in the field of dance choreography. This paper proposes a music-dance generation model based on generative adversarial networks (GANs), which consists of a generator and a discriminator. The generator employs the CL-K2M model, while the discriminator determines whether the generated dance sequence is authentic under the condition of known music. Additionally, the model calculates the pose differences between two frames based on the differences in degrees of freedom, and combines dance movement characteristics with motion interpolation techniques to achieve smooth transitions between two sequences. The dance movements generated by the designed model achieved the highest average user ratings in three metrics: rhythm (7.57), coordination (7.35), and style retention (8.45), demonstrating significantly superior performance compared to similar model algorithms.
Copyright (c) 2026 Ruiqi Peng https://creativecommons.org/licenses/by/4.0/
Exploration of Intelligent Optimization Algorithm for Risk Management in Green Financial Market Driven by Science and Technology Innovation
Green finance driven by science and technology innovation, as an innovative financial model, is facing problems such as weak risk management prediction ability while developing rapidly worldwide. Green finance driven by science and technology innovation, as an innovative financial model, is rapidly developing globally while it faces challenges such as insufficient incentive and constraint mechanisms and weak risk management prediction ability. Based on this, this paper introduces the IPSO-BP model for green finance market risk prediction model. The model uses BP neural network to process green financial market data, and introduces the IPSO algorithm to optimize the parameters of the BP network, aiming to enhance the model's prediction ability of green financial market risk. The results of the study on the actual dataset found that the prediction accuracy of the IPSO-BP model on the green financial market reached a maximum of 95.38%, and the absolute error of most of the prediction results is less than 0.06. Relying on the risk prediction results of the green financial market obtained by the IPSO-BP model, it can assist investors in choosing a more reasonable investment strategy, and ensure the stable operation of the green financial market at the same time, and enhance the risk control ability of investors. , and enhance the risk control ability of investors.
Copyright (c) 2026 Zhe Dong https://creativecommons.org/licenses/by/4.0/
Synchronized Generation of Appearance and Behavior of Cinematic Animated Characters Using Deep Generative Models
The continuous development of depth generation technology has improved the efficiency and quality of animated character generation. In this paper, we construct a cascade classifier based on depth generation technology, use the nearest neighbor difference to adjust the size of the expression region and complete the feature screening to determine the face expression feature set. By designing the loss function, the style consistency of the generated expression images is maintained. Introduce foreground mask mechanism and add expression magnitude discriminator in the expression editing model to improve the quality of expression generation. Using behavioral probabilistic finite automata (BPFA) to constrain the uncertain behavior generation of animated characters, and improving the fitness of generated behaviors and expressions through probabilistic calculation. The study shows that the animated character generation frame rate of this paper's method is >90f/s, the number of textures is >60MB, and the accuracy is high in 4 angles. During expression editing, this paper's method achieves stable convergence with only 59 iterations, and has the best effect on extracting the feature points of the face in the frontal angle. The generated movie-level animated character expressions and behavioral synchronization effects are all greater than 90%.
Copyright (c) 2026 Wei Peng https://creativecommons.org/licenses/by/4.0/
Combining Edge Computing to Enhance Real-Time Control and Low-Latency Response of Line Robots
In the context of real-time control and latency response scenarios for production line robots, this study focuses on optimizing the “cloud-edge-end” three-layer heterogeneous deployment based on edge computing. Multi-agent reinforcement learning methods are employed for task scheduling optimization, enabling distributed control to reduce resource waste and further improve load balancing. Dynamic event-triggered communication optimization reduces latency while minimizing network resource consumption. Through simulation testing, applying this method to optimize real-time control latency on the cloud platform achieved over 85% higher latency reduction compared to traditional cloud platform optimization tests. The system operates stably in dynamic complex scenarios, providing both theoretical and practical research and exploration into production line robot control optimization.
Copyright (c) 2026 Jianjia Qi https://creativecommons.org/licenses/by/4.0/
Research on User Preference Modeling and Data Enhancement Based on Generative Adversarial Networks in Precision Recommendation for E-Commerce Platforms
With the advent of the intelligent internet era, e-commerce has also experienced explosive growth, leading to intensified competition among various e-commerce platforms. To accurately identify user preferences on e-commerce platforms and eliminate irrelevant items, this paper proposes a user-demand-based generative adversarial recommendation algorithm based on generative adversarial networks (GANs). This algorithm consists of multiple generators, with a discriminative model evaluating the generated items and providing feedback to the generative model to continuously improve it, ultimately recommending items with high similarity to the generated items. Comparative experiments were conducted on two datasets, and the model training was completed using the proposed model. Experimental simulations were conducted on the MovieLens-100K dataset. The experimental results indicate that the proposed algorithm can effectively improve accuracy. UR-GAN has advantages such as fast convergence speed and a stable training process, making the practical value of the recommendation system based on UR-GAN relatively high.
Copyright (c) 2026 Lanyan Yang https://creativecommons.org/licenses/by/4.0/
Algorithmic Research on Integration and Optimization of Civic and Political Teaching Resources of University English Courses under Digital Transformation
This paper utilizes and continuously improves the teaching resources of Civics and Politics in university English courses from two aspects. On the one hand, K-means clustering algorithm, distance function optimization method and SimHash method are introduced to effectively classify the existing teaching resources and extract key features to provide data support for dynamic retrieval. On the other hand, through ontology construction, persistence algorithm and teaching resources optimization effect evaluation model, the unavailable teaching resources are uploaded to optimize the resource library and quantify the optimization effect to improve the utilization efficiency of resources. The redundancy ratio of integrated information for resource classification and retrieval is less than 2%. The classification accuracy rate and classification effect F1 value are both greater than 90% and 0.9. The resource search rate is above 91%.
Copyright (c) 2026 Ronglin Fu https://creativecommons.org/licenses/by/4.0/
A quantitative assessment model of the fit between local undergraduate colleges' major settings and regional industries based on regression analysis
This study integrates the coupled coordination degree model, relative development degree analysis, and the Cobb-Douglas production function regression model, which are used to quantitatively assess the fit between undergraduate colleges and universities' major structure and industry structure. Using 25 applied undergraduate colleges and universities in Area A as a sample, the empirical analysis is conducted for the period of 2018-2024.The overall coordination between the majors and industries of colleges and universities in Area A has steadily improved, with the degree of coordination increasing from 0.673 to 0.819, reaching the level of “highly coordinated”. However, the coordination between specific categorized disciplines and industries is not fully coordinated; agronomy and agriculture are precisely matched, with a coordination degree of 0.634 in 2024, but the synergy between science and technology and industry is seriously out of sync, with almost all of the seven years in a state of “low coordination”, which is an obvious shortcoming of industrial upgrading. Regression analysis further reveals its internal driving mechanism, every 1% increase in industrial output value can drive the percentage of students in science and engineering to increase by 0.463%, and the service industry is the core of the development of economics and management majors. The study not only diagnoses the effectiveness and problems of the synergy between the two, but also quantifies the industrial-economic contribution of the professional setting, which provides very powerful support for the region to formulate precise education-economic synergistic development policies.
Copyright (c) 2026 SuweiZhang, YaqinQin, LeiMeng, ZhihuaLiu, LihanYan https://creativecommons.org/licenses/by/4.0/
Application of Convolutional Neural Networks to Achieve Performance Enhancement of Robot Vision Inspection Systems for Industrial Production Lines
In response to the issue of dynamic complexity in robot vision inspection technology on industrial production lines, this paper proposes a method for robot vision inspection that combines convolutional neural networks (CNN) with background difference methods and spatio-temporal context target tracking technology. Using the established image database containing five types of workpieces—bearings, screwdrivers, gears, pliers, wrenches, and other five categories of workpiece images, and utilizing the proposed lightweight residual attention and decoupled attention mechanisms to design the FCN8s model. Experimental validation revealed that the model achieved an average pixel accuracy, average accuracy, and average IoU accuracy of 98.30%, 82.46%, and 73.62%, respectively, on the test set, meeting the real-time requirements of industrial visual inspection. In terms of accuracy, the recognition rate increased by approximately 15.97% compared to traditional methods, and the speed increased by approximately 83.26% to 84.65%. This paper adopts a multi-technology fusion solution to provide a more reliable and practical means for target detection and recognition in complex industrial environments, thereby providing a robot vision system for intelligent manufacturing.
Research on Curriculum Design and Teaching Effect Enhancement Methods Based on Intelligent Technology in Innovation and Entrepreneurship Education System of Colleges and Universities
The future career prospects and required skills for the courses being studied are one of the key reference directions for the design of innovation and entrepreneurship courses. This paper proposes an innovation and entrepreneurship online course design process based on the Moodle platform, comprising three key components: pre-course preparation, learning unit design, and course management and evaluation. The association rule mining algorithm is selected as the analysis algorithm for mining the relationships between job positions, courses, and required skills. A sample of 230 third-year students from D University's innovation and entrepreneurship course was selected as the research subject to generate association rules linking innovation and entrepreneurship job positions, professional courses, and the skills required for job positions. Under the generated association rules, innovation and entrepreneurship course instruction was conducted, and the course received positive evaluations from over 140 students in terms of “likability” and “enhancing critical thinking skills.”
Copyright (c) 2026 Hong Qian https://creativecommons.org/licenses/by/4.0/
Recognition and Enhancement of College Students' Self-Directed Learning Behaviors Based on Multilayer Perceptual Machines
This paper proposes a method for identifying college students' autonomous learning behaviors based on a multi-layer perceptron. The process of identifying and analyzing college students' autonomous learning behaviors is constructed from four aspects: data collection, data preprocessing, data analysis, and online learning outcomes. Based on the characteristics of college students' online autonomous learning behaviors, the multi-layer perceptron method is employed to assess the performance of their autonomous learning behaviors. Learning behavior variables are selected from three dimensions: behavioral engagement, social engagement, and cognitive engagement. The impact of each learning behavior variable on learning outcomes is analyzed. Four key factors are identified: video viewing count, assignment practice count, unit test count, and participation count. Based on the performance levels of these four key factors, different learning modes are proposed, and the self-directed learning behavior performance of students in each learning mode is analyzed. The correlation coefficient between behavioral engagement and online learning outcomes is 0.724, indicating a high correlation. Among the four learning modes, “rhythmic-type” learners maintained stable learning efforts and good unit test scores throughout the course, with stable autonomous learning behavior performance and outstanding autonomous learning behavior capabilities. Enhancing college students' learning behavior can cultivate students to develop “rhythmic-type” learning behavior.
Research on the Trend of Students' Ideological Dynamics in Civic Education Based on Time Series Prediction Modeling
This paper takes students' daily behavioral habits as the entry point to explore the changes of students' ideological dynamics in ideological education through behavioral analysis. The behavioral trajectory of students' school life is divided into five modules: course learning, school life, social interaction, employment and ideology and politics, and this is used to form a behavioral dataset of the research sample. After fusing the data from multiple sources to obtain the spatial and temporal fine-grained daily activity trajectories of the samples, the mathematical formulas for students' daily activities, daily behaviors and daily behavioral habits on campus are designed on the basis of which, as well as the calculation method for the intensity of the habits, the modeling method for students' daily behavioral habits is proposed. The extracted behavioral time feature sequences are input into the multivariate time series classifier, and the MLSTM-FCN model is determined to be the best model. Since its FCN branch has the problem of small sensory field and only supports single-item operation when extracting the sequence features, the Transformer encoder is used to replace the FCN unit, and the model sensory field is enlarged by introducing the form of multi-attention mechanism, so as to establish the time-series-based student abnormal behavior detection model. The model is calculated to get the intensity of the Civic Learning Resources Attention Behavior of Normal Behavior Students as 41.23% in the analysis of the change of students' ideological dynamics in Y college. It summarizes students' ideological cognition and behavioral changes through time series features, reflects students' growth patterns and effective education pathways, and points out the direction for updating and improving the model of civic education in colleges and universities.
Copyright (c) 2026 Haiyang Geng https://creativecommons.org/licenses/by/4.0/
International Law and The Regulation of Cross-Border Cyberattacks: Challenges and Solutions
In response to the global governance challenges posed by cross-border cyberattacks, this paper discusses the legal liability standards for cross-border cyberattacks, the conditions for the application of a state’s “right to self-defense,” and the pathways for the application of international law. Based on this, the author proposes establishing a “two-tier liability standard” system to address increasingly complex cyberattacks, while introducing the principle of “duty of care for cross-border harm” as a supplementary liability pathway to establish a logically consistent liability mechanism. The author proposes that the “effective control” standard applies to general cyberattacks, the “full control” standard applies to cyberattacks with characteristics of armed conflict, and the “duty of care” principle serves as a fallback mechanism. To address the aforementioned challenges, the author advocates a dual-track system for the application of international law—a complementary and mutually reinforcing model—specifically, the complementary integration of “hard law and soft law, international rules and domestic laws, and multilateral mechanisms and bilateral coordination mechanisms.” Regarding China's role and position in cyberattack governance, the author suggests that China should promote the concept of a “community of shared future in cyberspace” based on the principles of “equality, innovation, openness, and sharing” by actively safeguarding its own cyber interests as a leading factor (positive form). This approach ensures that national sovereignty is upheld while facilitating smooth international cooperation, maintaining a balanced relationship. This study addresses the research gap in the application of international law in cyberspace and provides a research basis and policy recommendations for states conducting cross-border cyberattacks, thereby advancing the establishment of legal norms in cyberspace.
Copyright (c) 2026 Xuejiao Qu https://creativecommons.org/licenses/by/4.0/
Extracting Periodic Features and Immunity of Electronic Communication Signals Using Adaptive Filtering Algorithms
With the increasing maturity of adaptive signal processing technology, signal interference caused during electronic communication signal processing has become increasingly prominent. Therefore, this paper proposes to use the analysis of signal periodic characteristics and interference characteristics as variables, and modify the update relationship of step length in traditional algorithms. This enables the improved adaptive filtering algorithm to maintain its fast adaptive characteristics and excellent adaptive interference performance even under low signal-to-noise ratio conditions, achieving good experimental results. It can effectively extract changes in the periodic characteristics of communication signals. The application of the variable step-size least-squares algorithm for tracking time-varying signals is explored, specifically by introducing changes in the adaptive forgetting factor during the update process. This allows the forgetting factor value to be more flexible and have a more appropriate numerical range, thereby further enhancing its interference suppression capability and numerical stability. Under the same signal-to-noise ratio conditions (5 dB), the convergence speed of the improved variable step-size least-squares algorithm increased by 67%, and the steady-state value decreased by 2.7 dB. Similarly, in the application scenario of improving the time-varying tracking of the recursive least squares algorithm, when the interference signal-to-noise ratio is 20 dB, the convergence time is only 17.8 ms. This paper analyzes how to effectively improve the signal processing capability and interference resistance performance in the electronic communication signal processing process, thereby enhancing signal processing quality and effectiveness.
Copyright (c) 2026 Lei Yang https://creativecommons.org/licenses/by/4.0/
Impact of a Knowledge Graph Technology Supported English Terminology Learning System on Mastery of English in Disciplines
In the face of the challenge of fragmented knowledge in English, the study starts from helping students master terminology knowledge in a more systematic way, and constructs an English terminology learning system driven by knowledge graph technology. By combining top-down and bottom-up approaches, the study utilizes the pre-trained BERT-BiLSTM-CRF model to extract and construct a knowledge graph for the subject of English at university. The system integrates key technologies such as efficient resource indexing, online collaborative text-based cross-domain mapping extension, and time-ordered knowledge inference. Firstly, 32703 knowledge point entities are mined from 20 core courses, and the accuracy of entity alignment can reach up to 95.38%, while the average F1 value of recognizing various types of entities is also stable at about 94.40%. In actual teaching, the experimental class using this system constructed in this paper, the performance improvement is very obvious - the average score directly rose from 68 to 86, to a certain extent, narrowing the achievement gap between students, and there is no low-scoring students. At the same time students were more active in the classroom, with the level of assessment of learning increasing from 3.58 to 4.77, and the intention to learn behavior increasing dramatically from 2.87 to 4.56. Satisfaction with the system and instructional design even reached a high score of 4.78 and 4.64 respectively. Regression analysis shows that the system itself explains 8.1% to 14.5% of the improvement in each English proficiency.
Copyright (c) 2026 Lin Gong https://creativecommons.org/licenses/by/4.0/
Intelligent Collaborative Algorithm Design in the Construction of Teaching Innovation Team Building System for Higher Vocational Teachers in the Information Age
In response to the challenges faced in the construction of teaching innovation teams at higher vocational colleges, including low efficiency and quality in resource integration and the absence of collaborative mechanisms, this paper designs a course resource construction process within the framework of swarm intelligence theory, comprising three stages: the preliminary organizational stage, the mid-term construction stage, and the post-construction refinement stage. To address the optimization needs of various resources and the coordination tasks of different types of workloads, a Markov decision process is introduced to achieve multi-dimensional resource and workload coordination and scheduling. A reinforcement learning framework based on collaborative relationship representation learning is proposed, consisting of three components: an agent value function network, a collaboration graph encoder, and a hybrid network. This framework is used to construct a multi-dimensional resource collaborative optimization algorithm based on reinforcement learning. The educational resource sharing platform established using this method demonstrates superior service performance compared to traditional educational resource sharing platforms, with an average throughput increase of 180.71 b/s and an average response time controlled within 5 seconds.
Copyright (c) 2026 Zheng Ran https://creativecommons.org/licenses/by/4.0/
Deep learning-supported multi-objective power system scheduling in an energy internet optimal scheduling environment
In the context of energy internet, new energy access makes the power system present multi-objective coupling, difficult security constraints, etc., and the traditional model-driven methods have limitations in scheduling efficiency and adaptability. Based on this, this paper proposes a PPO-DDPG model that combines deep reinforcement learning and proximal policy optimization algorithm. The model acquires the dynamic timing data of the power system with deep reinforcement learning and designs constraints on its states, actions, rewards and so on. The PPO algorithm is then introduced to update the network parameters as a way to improve the dynamic scheduling effect on multi-objective power systems. Experiments show that the new energy consumption rate of the PPO-DDPG algorithm after convergence is as high as about 97.5%, which is about 30% higher than that of the PPO algorithm, and the average survival time as well as the reward value are significantly better than that of the existing methods. Therefore, relying on deep learning technology to empower multi-objective power system scheduling is more economical, ensuring that the power system can obtain optimal economic benefits when accessing different types of energy under high uncertainty.
Copyright (c) 2026 Qian Liu https://creativecommons.org/licenses/by/4.0/
A Study on the Dissemination and Acceptance of Film Music in Different Cultural Contexts Based on Big Data Analysis
This study employs big data analysis technology, combined with social network analysis and agent-based communication models, to explore the dissemination mechanisms and acceptance differences of film music across diverse cultural contexts. “Work-to-work” and “user-to-user” communication networks are constructed, with network structural characteristics quantified using metrics such as degree centrality and intermediate centrality. Agent-based models simulate individual communication behaviors to reveal the influence of communication intent and relationship strength on communication efficiency. Empirical analysis using multi-source data reveals that an individual's status within their group is positively correlated with their dissemination behavior, and relationship strength is associated with the scope of music dissemination. As age increases, the proportion of individuals who find film music just barely acceptable gradually increases (10% and 16% for young adults, 16% for middle-aged adults, and 26% for the elderly), while the proportion who find it completely unacceptable also gradually increases (0% and 8% for young adults, 10% for middle-aged adults, and 14% for the elderly). The acceptability of film music varies little across cultural environments, with respondents from all four cultural environments reporting acceptability rates of over 80% for the example film music.
Copyright (c) 2026 Yuan Ren https://creativecommons.org/licenses/by/4.0/
Construction of Red Agricultural Culture Digital Resource Base Based on Data Mining and Its Application in Art and Aesthetic Education
With the continuous development of technologies such as big data and cloud computing, this paper utilizes data mining techniques to extract, analyze, clean, convert, filter, and integrate red agricultural cultural digital resources, storing the public metadata in a data warehouse. This study mainly focuses on students enrolled in the “Teach You a Trick” aesthetic education course offered by our institution. through a questionnaire survey to investigate the actual differences in digital resource needs among students of different genders, disciplines, and grades within the red agricultural cultural digital resource repository in the context of art and aesthetic education. The results show that 92.78% of students believe that accessing red agricultural culture digital resources is generally easy, fairly easy, or very easy. 62.7% of students believe that the proportion of their learning time spent using red agricultural culture digital resources is below 20% or between 21% and 40%, indicating that the usage time of red agricultural culture digital resources in the learning process needs to be improved.
Copyright (c) 2026 Nannan Yang https://creativecommons.org/licenses/by/4.0/
Exploration of Using an Artificial Intelligence-Assisted Adaptive Learning Platform to Enhance the Effect of Interactive Teaching in College English Classrooms
University English courses have poor classroom interaction due to the limitations of learning time and environment, so there is an urgent need to establish a benign teacher-student interaction. In this paper, based on the learning model of adaptive learning platform, a fuzzy neural network system is utilized to design the outline of adaptive learning platform. The theoretical model of teaching interaction is introduced, and the English adaptive learning platform based on ANFIS system is designed under the conceptual interaction, information interaction and operational interaction. Teaching interaction behaviors in the English classroom are analyzed, and the teacher-student verbal and behavioral rates of applying the adaptive learning platform for learning are comparable, basically staying at about 50%. Using the random survey method combined with pre and post-tests to measure the changes in students' experience of classroom interaction, after the implementation of the adaptive classroom, students' classroom experience changes significantly, students are interested in the English classroom, an increase of 31.43% compared with the previous, indicating that the implementation of the adaptive English learning platform, the classroom is welcomed by the students, and the effectiveness of the teaching and learning has been significantly improved.
Copyright (c) 2026 Hong Meng https://creativecommons.org/licenses/by/4.0/
Study on the Construction of Immersive Learning Scene in the Construction of “Integration of Large, Medium and Small” Civics Course Based on Virtual Reality Technology
The improvement of the integrated construction of ideological and political education promotes the equitable development of education. To enhance the realism and construction efficiency of immersive learning scenarios, this paper proposes an improved registration algorithm (RANSAC) based on the determinant point process (DPP) to optimize the sampling accuracy of image feature points. By establishing a collaborative education platform for primary, secondary, and higher education, a k-DPP sampling strategy is designed to enhance the uniformity of feature point spatial distribution, achieving high-precision learning scenario integration. Through comparative and control experiments, the effectiveness of the constructed immersive learning environment in assisting ideological and political education is validated. The study shows that the constructed technology achieves a maximum mIoU of 0.963 across 10 scenarios, with inference times ranging from 65 to 80 seconds, demonstrating good feature extraction performance and faster scenario construction speeds. The experimental class achieved an average satisfaction score of 9.52 for the immersive learning scenarios, with flow experience scores in all 8 dimensions exceeding those of the control class, and learning psychological states maintained at a high arousal level.
Copyright (c) 2026 Hongxia Shao https://creativecommons.org/licenses/by/4.0/
Research on Interactive Learning Platform Based on Speech Recognition and Generation Technology in Higher Education English Classroom Teaching
The application of an interactive learning platform based on speech recognition and speech synthesis technologies in college English classrooms can facilitate effective speech-based interactive learning between teachers and students. This paper analyzes the use of interactive learning platforms in college English education to explore methods for enhancing learning outcomes. The teaching platform developed in this paper can receive and process speech data using speech recognition technology to assess the accuracy of students' pronunciation during reading aloud and provide feedback to learners. Additionally, speech synthesis technology is utilized to create authentic and diverse teaching scenarios for speech dictation, thereby enhancing students' listening comprehension skills. Furthermore, speech synthesis technology is used to help students “imitate” real people and “immerse” themselves in teaching scenarios and real-life teaching situations, thereby increasing students' in-class listening and speaking time and achieving constructive application of student learning evaluations. Research results show that the use of this teaching platform has improved human pronunciation accuracy from 76.5% to 91.2%, increased the number of speaking instances from 3.6 to 5.7, and significantly enhanced student participation and interest in learning. The platform can intelligently analyze learning trajectories and provide personalized feedback through interaction with individuals, serving teachers in developing differentiated instruction for students. By applying speech recognition and speech generation technology to analyze learners' pronunciation and provide feedback, this study assists English classrooms in achieving a new form of interactive education, which holds significant positive implications for the effective implementation of English education and teaching.
Copyright (c) 2026 Bing Shen https://creativecommons.org/licenses/by/4.0/
Exploration of Gymnastic Movement Analysis and Skill Improvement Paths Based on Long and Short Term Memory Networks
For a long time, the diversity and complexity of gymnastics movements have made it more difficult to carry out the analysis of gymnastics movement evaluation, which is an obstacle to the improvement of gymnastics skills. In order to improve the recognition effect of gymnastics movements, this paper combines OpenPose and LSTM to establish a HOPL model for gymnastics movement recognition and evaluation. The model combines the original OpenPose network with a high-resolution network to achieve multi-feature fusion of gymnastic actions, and introduces the LSTM model to recognize or predict complex actions and behaviors by using the temporal information of skeleton sequences. Experiments show that the recognition accuracy and global F1 score of the HOPL model are 90.75±1.76% and 86.72±0.93%, respectively, and the computational consumption is low, so it is feasible to apply it to the evaluation of gymnastic movements. Relying on the application of deep learning technology in the field of gymnastics movement recognition, it can provide optimization strategy support for gymnastics movement skill improvement.
Copyright (c) 2026 Dan Mo, Yintong Wang, Mengyun Hu https://creativecommons.org/licenses/by/4.0/
Construction of a Model for Evaluating the Effects of Regional Ethnic Autonomy Laws and Regulations Based on Differentiated Big Data Analysis
This paper constructs a model for evaluating the effectiveness of laws and regulations on ethnic regional autonomy based on differentiated big data analysis. It innovatively integrates the Fuzzy Best-Worst Method (FBWM), the CRITIC objective weighting method, and the principle of minimum discriminative information to form the FBWM-CRITIC combined weighting model. First, the current state of legislation for autonomous regulations and single-issue regulations is systematically reviewed, establishing four primary indicators: legal effectiveness (A1), political effectiveness (A2), economic effectiveness (A3), and social effectiveness (A4). These are further broken down into 18 secondary indicators and over 50 tertiary observational indicators. Through combined weighting, the core weights were determined: social effects (A4) accounted for 35.1%, political effects (A2) for 23.9%, highlighting the critical importance of human rights protection and public services; economic effects (A3) accounted for only 20.2%, with the lowest weight (0.034) among sub-indicators being technological development (B12). Model validation indicates that the FBWM consistency ratio (CR = 0.041–0.059) significantly outperforms AHP (CR = 0.075–0.132), and the combined weighting error rate is 23.7% lower than traditional additive/multiplicative synthesis methods. Applying this model to evaluate the implementation effectiveness of the five autonomous regions from 2015 to 2024 reveals that all dimensions are at the “qualified” level (60–70 points), with economic effectiveness being the best (66.79 points) and legal effectiveness being the weakest (63.06 points). The overall effectiveness improved by 47.2% over the decade (from an average of 0.443 in 2015 to 0.652 in 2024), Xinjiang (0.708) and Ningxia (0.692) maintained their lead, while Tibet showed significant fluctuations (from 0.385 in 2015 to 0.584 in 2024). Markov chain analysis revealed that the probability of low-level regions being locked in reached 88%, while high-level regions (Category IV) achieved 100% stability and exerted positive spillover effects on neighboring regions (increasing the transfer probability by 12%).
Copyright (c) 2026 Yuwen Shi https://creativecommons.org/licenses/by/4.0/
Research on temperature control optimization method of ice storage refrigeration system empowered by Internet of Things in digital transformation environment
Aiming at the problem of high energy consumption and low level of intelligent operation and maintenance of ice storage cold air conditioning system, this paper analyzes the intelligent operation and maintenance design of ice storage cold air conditioning system empowered by Internet of Things (IoT) technology. In order to optimize the control of temperature during the operation of ice storage cold air conditioning system and achieve the goal of minimizing the operation energy consumption, this paper adjusts the inertia weight of particle swarm optimization algorithm, introduces the artificial immunity idea to form the immune particle swarm algorithm, and strengthens the algorithm's ability of local optimization. Through the AI-PSO algorithm to optimize the ice storage air conditioning system PID controller parameters to solve the objective function. The optimization results of the ice storage air conditioning system show that under different ratios of rated demand loads, the optimization results of the AI-PSO algorithm have smaller fluctuation values and the system consumes less energy. In the same cooling cycle, the AI-PSO algorithm is 27.29% and 8.08% smaller than the mainframe-first and ice-melting-first control methods, respectively, with significant peak-shaving and valley-filling effects, and better energy-saving effects and control quality. Compared with the host-first and ice-melting-first methods, the method has better temperature control effect and economic benefits.
Copyright (c) 2026 Haitao Sang, Xiaoyan Fan https://creativecommons.org/licenses/by/4.0/
A Study on Digital Transformation of Financial New Quality Productivity to Promote Supply Chain Modernization in the Rural Industrial Chain
Promoting the development of new-quality financial productivity is an intrinsic requirement for the digital transformation of rural industrial chains and supply chains. This article is based on panel data from 30 provinces in mainland China from 2014 to 2024 and uses interaction effect models, mediation effect models, and other empirical tests to discuss the impact of new-quality financial productivity on the modernization of rural industrial chains and supply chains and its mechanisms. The results indicate that the estimated coefficient for financial new-quality productivity is 0.248, suggesting that financial new-quality productivity has a significant driving effect and positive moderating effect on the modernization of industrial chains and supply chains. The impact coefficients of financial new-quality productivity on the modernization of rural industrial chains and supply chains vary across regions: eastern region (0.5168) > northeastern region (0.1722) > central region (0.1570) > western region (0.0999).
Copyright (c) 2026 Qianyun Yang https://creativecommons.org/licenses/by/4.0/
Combining Multimodal Learning Models to Enhance Contextual Comprehension in English Translation
This study proposes a novel English translation method that integrates multimodal learning with contextual theory to enhance the system's ability to understand context and cultural context. By introducing a multimodal knowledge graph, integrating information sources such as text and images, and employing a relational graph attention network for deep encoding, the method effectively constructs semantic association embedding representations. The innovatively designed cross-modal alignment module significantly optimizes the semantic alignment capability between images and text, enabling the system to handle cultural metaphors and professional terminology with greater precision. Experimental results show that this method achieves scores of 87.6% and 86.3% in semantic accuracy and cultural equivalence, respectively, with fluency scores improving from 7.2 to 8.9, demonstrating its strong advantages in translating complex contextual tasks. This research not only expands the boundaries of translation technology but also provides a new paradigm for the integrated development of linguistics, artificial intelligence, and cross-cultural studies. The findings are expected to have broad application value in international communication, translation education, and the development of intelligent translation systems.
Copyright (c) 2026 Rui Shi https://creativecommons.org/licenses/by/4.0/
A Study on Optimizing Physical Education Curriculum Scheduling Using Data Flow Analysis Algorithm
In order to improve the speed and accuracy of large and complex information processing, the use of data flow analysis technology and to replace the traditional manual scheduling is an inevitable requirement for the quality improvement of higher education. Based on the basic concept of data flow analysis, this paper proposes the use of data flow analysis in different situations. Considering the actual demand of constraints, the scheduling problem is divided into hard and soft constraints, and a multi-objective optimization constraints mathematical model is constructed for physical education course scheduling. Using genetic algorithm, the sports course scheduling problem is optimized and solved. Test the performance of the algorithm in the actual operation and the use of the effect, the model in the completion of the iteration score of 97.058, the whole process takes no more than 2.5 s. Before and after the optimization of scheduling program students change classroom per capita time consumption of 10.04 min and 6.93 min, respectively, the passage of the time consumption reduced by 30.98%, the algorithm used in this paper effectively reduces the passage of time, the physical education course schedule The algorithm used in this paper effectively reduces the passage time and plays an optimizing role in the schedule of physical education courses.
Copyright (c) 2026 Yan Shang https://creativecommons.org/licenses/by/4.0/
An Empirical Study on the Effect of Contextualized Learning of English Vocabulary on Long-Term Memory Retention Supported by Augmented Reality Technology
With the development of augmented reality (AR) technology, the level of contextualization has been continuously improved. This paper utilizes ZAPPAR software to design and implement AR vocabulary teaching materials, aiming to enhance the realism of contextual learning in the classroom. A control experiment was conducted to verify the effectiveness of contextual learning under AR technology. Student learning data was collected through questionnaires and other methods, and Pearson correlation coefficients were further introduced to calculate the association between contextual learning ability under AR technology and students' long-term vocabulary acquisition. The experimental results show: The average scores for short-term and long-term vocabulary memory in the experimental group were 91.034 and 90.768, respectively, with a memory decay change of only -0.266 points, which outperformed the control class and the comparison method. The correlation coefficient between the AR-supported contextual learning method and long-term vocabulary acquisition was 0.35. Additionally, the regression coefficients for the three categories of vocabulary were 9.765, 32.821, and 0.503, respectively, indicating a positive effect on vocabulary memory.
Copyright (c) 2026 Dandan Shi https://creativecommons.org/licenses/by/4.0/
Research on Artificial Intelligence-driven Resident Demand Forecasting Model for Digital Community Governance
This study starts from two aspects, data-driven and demand insights, to realize accurate forecasting of residential demand. Firstly, the raw data are sculpted and feature extraction of load fluctuation is performed using mutual information and principal component analysis. And an intelligent prediction model Bi-ITCN-Att, a bidirectional improved temporal convolutional network incorporating a self-attention mechanism, is built.Innovatively, the article introduces the Kano model as a demand decoder to analyze the motivation of residents' electricity consumption behavior from the perspective of humanistic demand. The model performs well in predicting load in a neighborhood, with a root mean square error of 0.027 and a MAPE of only 1.86%, and the prediction accuracy is significantly better than that of the original TCN and Bi-ITCN models. The analysis of 369 valid questionnaires reveals that 54.47% of the residents regard circuit fault warning (ESR1) and other as the bottom line demand M that must be guaranteed, while the laws such as AI energy conservation advice (ECF2) are regarded as glamorous attributes A. The quadrant diagram of the demand strategy drawn through the analysis of Better-Worse coefficients further proclaims that the key to improving the satisfaction lies in the implementation of the electricity price services such as reminders, which has a satisfaction coefficient of 0.549, while the defense of emergency security must be maintained. The study provides a set of decision support system for digital community governance that is not only accurate in data, but also handy in human needs, thus promoting the community energy service from passive response to a new stage of proactive and humanized intelligent governance.
Copyright (c) 2026 Jinglin Tan https://creativecommons.org/licenses/by/4.0/
Optimization Study of Intelligent Robot Production Line Scheduling Problem Based on Markov Decision Process
With the rapid development of science and technology, industrial technology has continued to advance and improve. Addressing the issue that traditional Markov prediction methods often yield results that deviate from reality, this paper proposes an optimization solution method based on reinforcement learning. It employs a deep deterministic action strategy to compute an infinite-dimensional semi-Markov queuing model (where the queuing system control time is positive infinity), thereby achieving the objective of model optimization. Taking the parts production task O = {2, 12, 19} as an example, the effectiveness of the proposed method is validated by verifying the mold scheduling process solved using the reinforcement learning algorithm. Research shows that the average time spent on each stage of part production using the pre-optimization scheduling strategy was 27.90167, while the average time after optimization was 12.6533. Therefore, applying the reinforcement learning-based optimization method significantly improves the operational efficiency of intelligent robot production line scheduling and ensures reliability.
Copyright (c) 2026 Shuai Yang https://creativecommons.org/licenses/by/4.0/
Optimization Effect of Nonlinear Denoising Algorithm of Audio Signal in Piano Performance on Aesthetic Performance of Piano Timbre
In recent years, with the rapid development of computer science and information technology, research on the acoustic aspects of audio signals has gradually become a hot topic in the field of music. Addressing the issue of low accuracy in current instrument audio recognition, this paper proposes a set of nonlinear denoising algorithms to optimize the aesthetic performance of piano tones. Using WAV files of Yamaha YA128EX piano performances as experimental data, MATLAB simulation results show that within 10s, 20s, and 30s, the root mean square errors (RMSE) of the signal processing using this method are 4.49%, 3.30%, and 3.36%, respectively, while the RMSE of other methods are 5.80%, 6.88%, and 3.76%, respectively. Therefore, the proposed method achieves more significant results in piano audio signal feature extraction, with higher extraction accuracy, and the beam integration of the recognition output is significantly improved.
Copyright (c) 2026 Xiting Yang https://creativecommons.org/licenses/by/4.0/
Research on the assessment model of vocational education employment matching degree based on multiple regression analysis
At present, vocational education pays more attention to the comprehensive quality education of the educated, and it has become the consensus of higher vocational colleges and universities to fully improve the degree of students' employment matching. This paper proposes a vocational education employment matching degree assessment model based on entropy weight method and fuzzy comprehensive evaluation, and introduces the multiple linear regression model to analyze the specific factors affecting the vocational education employment matching degree. Firstly, the index system of vocational education employment matching degree assessment is established, the entropy weight method is used to solve the index weights, and the fuzzy comprehensive evaluation model is combined to realize the assessment of vocational education employment matching degree. Then, using the data of graduates from 50 vocational colleges and universities in China as support, we constructed benchmark regression and threshold regression models to explore the path of vocational education employment matching degree improvement. It is found that the fuzzy comprehensive evaluation model can realize the accurate assessment of vocational education employment matching degree, the level of students' employability has a significant positive effect on vocational education employment matching degree, and the promotion effect on vocational education employment matching degree is stronger when the level of regional economic development is lower than 16.21%. Therefore, vocational education needs to optimize the design of students' employment guidance courses, reasonably promote the reformative development of regional economy, and then promote the improvement of vocational education employment matching degree and enhance the quality of vocational education talent cultivation.
Copyright (c) 2026 Jing Xiao https://creativecommons.org/licenses/by/4.0/
Modeling the impact of structural changes in international trade on economic growth based on principal component analysis
China's domestic demand is weak, the growth of domestic demand is constrained, some industries are overproducing, and many enterprises are facing great challenges in production and operation. This paper focuses on analyzing the influencing factors of export trade structure, constructing a regression model according to the related theory of influencing factors, and analyzing the covariance of variables and determining the variable indicators affecting economic growth through principal component regression analysis. Based on the BEC criterion and the research experience of the previous researchers, the international trade structure indicator model applicable to China was established, and the least squares method in multiple linear regression was used, and the test was carried out according to the analytical steps of the Granger test. The adjusted R square is 0.4686, the regression variance F value is 7.198, the significance is 0.0245, less than 0.05 indicates that the regression equation is valid and the proposed variables can be used for role analysis. The results of regression analysis show that every 1% increase in the coefficient of comparative advantage raises economic growth by about 0.0635%. The change of international trade structure has a positive impact on economic growth, but the current change of international trade structure has a certain path dependence, so there is a lag in the performance of the response of the change of international trade structure to economic growth.
Copyright (c) 2026 Zhiqing Xia, Yan Wang, Ge Song https://creativecommons.org/licenses/by/4.0/
Exploring the Construction of English Translation Corpus and Application of Translation Teaching in Colleges and Universities Based on Parallel Computing
The article first collects Chinese-English bilingual corpus through web crawler technology, and then constructs a preliminary Chinese-English bilingual corpus by unifying coding and filtering to improve the quality of the corpus. After that, a distributed system based simple Bayesian algorithm is used to realize large-scale text mining and classification. Then a parallel corpus of English-Chinese translation in colleges and universities is constructed, and a model of the corpus assisting English teaching in colleges and universities is designed, and the practical application effect of the model is analyzed. The results show that: the classification accuracy of this paper's algorithm on the experimental corpus can be stabilized above 95%, and this paper's algorithm works well in the parallel computing performance test, which can straightly keep a lower scale ratio, so that the clusters can be more fully utilized, and has better parallelism. Under the corpus-assisted English teaching practice in colleges and universities, the English proficiency of the students in the experimental class is significantly higher than that of the control class (p<0.05), especially in the “vocabulary and grammatical structure, translation, writing” questions, there is a significant difference in the effect between the control class and the experimental class.
Copyright (c) 2026 Weihui Hong https://creativecommons.org/licenses/by/4.0/
Research on the Application of Shortest Path Algorithm in the Optimization of Higher Education Curriculum System
Traditional higher education course recommendation methods mostly rely on static rules or simple associations, making it difficult to dynamically generate optimal learning paths that match learners' characteristics. In this study, we propose a framework for optimizing higher education course system that integrates knowledge graph and shortest path algorithm. The framework first constructs a knowledge map of higher education courses oriented to knowledge points, which formally represents the complex logical relationships of the courses. The learning path planning problem is then converted into a problem of finding the optimal path on the knowledge graph, and an ant colony algorithm is introduced to solve the problem, which describes pheromones, heuristic functions, and other major objects, and dynamically plans the shortest learning path for the learners. The superiority of the algorithm in learning path planning rationality and personalization is verified through parameter optimization experiments and algorithm simulation experiments. The system can effectively help learners to carry out informative and high-quality learning, which provides important help to the Ministry of Education to cultivate high-quality talents.
Copyright (c) 2026 Liu Lin https://creativecommons.org/licenses/by/4.0/
Application of Data Mining Methods in Analyzing Learning Habits of Chinese Language Chinese Education Students and Improving Teaching Strategies
This study utilizes multidimensional background information data from 280 students majoring in Chinese language education at a university. Employing data mining and K-means clustering techniques, the study conducts qualitative and quantitative statistical analysis and mining of students' learning data to gain insights into their learning motivations, explore their learning profiles and habits, and uncover the correlations between learning behaviors and academic performance. This research aims to provide theoretical support for teachers in developing teaching strategies and improving instructional practices. Analysis indicates that over 70% of students engage in outdoor activities three or more times, and those who consistently eat breakfast on time and review before class demonstrate superior academic performance. Strict attendance requirements in the classroom are correlated with final course grades. Based on these correlations, teachers can adjust their teaching strategies and methods to enhance instructional effectiveness.
Copyright (c) 2026 Xiaoyu Yang https://creativecommons.org/licenses/by/4.0/
Research on the Application of Multi-Objective Decision Making Algorithm and Teaching Quality Improvement in Physical Education Teaching in Colleges and Universities
In this paper, in order to solve the multi-objective decision-making problem of teachers in physical education teaching, the weights of indicators at all levels in the hierarchical analysis method physical education teaching quality index system are introduced to be assigned. Then the fuzzy decision theory model is used to expand the one-dimensional decision-making of the fuzzy comprehensive evaluation model into multi-dimensional decision-making, to help college physical education teachers to complete the multi-objective decision-making of the teaching process and improve the quality of teaching. The results show that: in the fuzzy comprehensive evaluation index system of physical education constructed in this paper, the weight of the first-level index teaching process evaluation is the highest (0.4363), and the weight of the teaching background is the lowest (0.0611); among the second-level indexes, the top three indexes are the elements of the physical education curriculum, the organizational and systematic guarantee of the physical education teaching in the school and the overall performance of the teaching activities, and the comprehensive weights are 0.2015, 0.168 and 0.1409, respectively. 168 and 0.1409 respectively, and the weights of the rest of the indicators range from 0.0122 to 0.108, which shows that the importance of each indicator varies greatly. In addition, this paper summarizes nine group paths to improve the quality of physical education teaching, which can explain about 80% of the cases of physical education teaching quality improvement in colleges and universities.
Copyright (c) 2026 Yage Yang https://creativecommons.org/licenses/by/4.0/
Cross-Cultural Climate Communication in Podcasts: A Comparative Discourse and Engagement Analysis
Climate change is one of the most pressing global challenges, necessitating effective communication strategies to raise public awareness and promote action. As a growing digital medium, podcasts have emerged as influential platforms for climate communication, with broadcasters playing a crucial role in shaping public perceptions. This study explores the role of broadcasters in addressing climate change through climate-themed podcasts from a cross-cultural perspective. By analyzing podcast content from diverse cultural contexts, this research examines how linguistic styles, narrative strategies, and engagement techniques influence audience reception and environmental attitudes. The study adopts a qualitative approach, employing content analysis and comparative methods to identify key patterns in climate communication across different regions. Conceptual insights indicate that cultural factors significantly impact how climate issues are framed and conveyed, with variations in directness, emotional appeal, and scientific emphasis. Additionally, the effectiveness of broadcasters' delivery styles, including tone, speech patterns, and interaction with listeners, shapes audience engagement and trust. This research contributes to the growing field of environmental communication by providing insights into how podcasting can serve as a strategic tool for climate advocacy. The study also offers practical recommendations for broadcasters to enhance cross-cultural climate communication and foster greater public engagement in climate action.
Copyright (c) 2026 ZiJun Wang https://creativecommons.org/licenses/by/4.0/
User Image Modeling and Interaction Optimization Strategies in Interaction Design of Short Video Parenting Platform Construction in Colleges and Universities
With the rapid development of science and technology, students' behavior of using short video parenting platform to watch videos in their daily life is becoming more and more popular. Therefore, the article proposes a user classification method based on the improved FRM model for college short video parenting platform, determines the weights of the four indicators in the RFMS model through the combined assignment method, and then applies the K-means clustering algorithm to analyze the users by clustering. And on this basis, a short video recommendation algorithm based on association rules is proposed aiming to improve the accuracy of short video recommendation, and its effect is verified through experiments. Finally, an interaction optimization strategy based on college short video parenting platform is proposed in combination with the basic user profile. The article analyzes the changes in the distribution of topics and the number of topic types in the recommendation list obtained from the model of this paper and the collaborative filtering recommendation model. The results show that the user-based collaborative filtering recommendation model declines relatively sharply in the three indicators. The degree of decline becomes significantly smoother at [30,60], while the overall degree of decline of the three indicators of this paper's model is relatively smooth. From this, it can be concluded that the model in this paper has obvious advantages in improving the diversity of recommendation results.
Copyright (c) 2026 Yan Yang https://creativecommons.org/licenses/by/4.0/
Teaching Practice of Consumer Clicking Behavior Recognition Algorithm on E-Commerce Platform in E-Commerce Education of Higher Vocational Colleges and Universities
With the goal of accurately mining consumer clicking behavior data on e-commerce platforms, this paper proposes a clustering algorithm based on FCM fuzzy operations, which is applied to e-commerce education in higher vocational colleges and universities. The collected e-commerce platform consumer data are normalized, and the FCM fuzzy clustering analysis algorithm is applied to the acquired two data structures, namely, dissimilarity matrix and data matrix, and after obtaining the clustering map, the data points within the space of the data structure are used as sample inputs to search for the constituent properties of the sample points and the space of the data structure, and to obtain the e-commerce platform consumer data mining results. The clustering results of the algorithm categorize the e-commerce platform consumers into three categories: selective type, comparison type and decisive type, which improves the resource allocation efficiency of the enterprise and further enhances the enterprise profit. The effect of teaching practice shows that the students in the experimental group improved their average grade by 4.3 points compared with the control group through the application of the teaching mode based on the clicking behavior recognition algorithm of consumers on the e-commerce platform. It shows that this algorithm provides a more scientific and perfect teaching mode for students of higher vocational e-commerce majors and enhances the effectiveness of teaching.
Copyright (c) 2026 Yi Yang, Qiang Li https://creativecommons.org/licenses/by/4.0/
Competitive Strategies and Operational Optimization of Music Enterprises in the Context of Globalization
In the music market driven by the trend of globalization, different subject music enterprises are limited by their positions and perceptions, and have higher quality strategy needs in competition and operation. This paper adopts the emerging evolutionary game theory to explore the equilibrium stability of decision-making behavior of market players with limited rationality and access to limited information, as well as the factors affecting it. On the basis of the theory of evolutionary games, the mathematical definition of stable equilibrium state under evolutionary stable strategy is designed. At the same time, we further analyze the equilibrium stability of the two-party, two-strategy evolutionary game, and comprehensively construct an analytical model of the evolutionary game. Analyze the development trend of mobile music business in the United States, South Korea, and Japan from 2008 to 2012 as the data samples for the study. The proposed evolutionary game analysis model is used to determine the Nash equilibrium stability zone in the music market competition, and analyze the changes of the average profit of different market players under different parameter values. In particular, when the music producers' concern about fairness is lower than 0.2, the average profit of both the music producers themselves and the music retailers show an upward trend as the music producers' concern about fairness grows.
Copyright (c) 2026 Xingang Shi https://creativecommons.org/licenses/by/4.0/
Multiple Regression Analysis for Tourism Management: A Path of Integration between Tourist Satisfaction Enhancement and Business Operations
In recent years, the tourism industry has gradually developed into a strategic pillar industry of the national economy and has become one of the important industries for development in China. In order to help tourism management, the study utilized socio-economic data and selected 13 indicators of tourism development driving factors. Principal component analysis was applied to extract the main driving factors affecting tourism development. A multiple linear regression model was used to study the interrelationship between tourism development and various driving factors, and the results of principal component analysis were verified by the goodness-of-fit test and the significance test of regression coefficients. The test of the fitted linear regression coefficient of determination R² = 0.994 indicates that the model fits the data well. The quantitative model of tourism development was established as F1=-6.18012+2.083ZLX1+0.496LX2+0.145ZLX3+1.154ZLX4. That is, for every 1% increase in socio-economic conditions, tourism service capacity, tourism resource attractiveness and infrastructure development, tourism development increases by 2.083%, 1.154%, 0.496% and 0.145% respectively. In addition, the study starts from the perspective of business operation and proposes to enhance employee training, improve employee welfare and good customer relations as a way to improve tourist satisfaction.
Copyright (c) 2026 Yonghe Yang https://creativecommons.org/licenses/by/4.0/
Research on stability analysis and control optimization method of DC microgrid based on numerical simulation
DC microgrid systems can better meet power energy demands and adapt to the future development characteristics of DC loads. Based on the structure of DC microgrids, this paper combines the operational characteristics of distributed power sources (wind power generation, solar power generation) to establish a mathematical model of the DC microgrid system. By introducing droop control into AVDCM control, fuzzy control is configured to use only the rate of change of bus voltage as input, forming an improved P-U droop coordinated AVDCM control. This establishes a control optimization model for the DC microgrid system. DC microgrid control optimization simulation experiments were conducted to explore the control optimization performance of the proposed DC microgrid control optimization model. Under grid-connected operation, both the 700V DC bus voltage and the 48V DC bus voltage can recover to a voltage-stable state within 0.1 seconds. In islanded operation, the output power of the photovoltaic panels rises to between 144 kW and 146 kW within approximately 0.7 seconds, reducing the power supply burden on the batteries. Under varying 700 V DC bus voltages, the load can operate reliably, and the 48 V DC bus voltage remains in a steady state.
Copyright (c) 2026 Fanhua Meng, Likui Yi, Rui Feng, Dongge Liu https://creativecommons.org/licenses/by/4.0/
Research on the Path of Improving Intercultural Communication Competence Based on Knowledge Mapping in the Cultivation of Business English Talents in Colleges and Universities
With the increasing development of globalization, intercultural communication in the context of globalization puts forward an urgent demand for intercultural talents. In view of this situation, this paper studies the construction and application of knowledge mapping in the field of business English courses in colleges and universities, and puts forward the method of intercultural communication competence enhancement based on knowledge mapping. First, we model a fine-grained learner portrait from multiple dimensions such as learners' learning styles, knowledge status, and ability information, and construct a knowledge map of the business English course, apply the ant colony algorithm to learning path planning, and use non-uniform initialization of pheromones and the addition of frequent relational sequences to provide effective solutions to the learning path planning problem. In the cross-cultural communicative competence path planning result species, the cross-cultural communicative competence path planning is summarized as the theory of cultural dimensions → cross-cultural communication barriers and coping strategies → cross-cultural applications in business scenarios → cross-cultural business writing and emails → practical exercises. Through empirical analysis with 110 students majoring in Business English in a university as the research object, the teaching based on the method of this paper is significantly better than traditional teaching in improving the emotional and behavioral dimensions of students' intercultural communicative competence. The mean value of intercultural communicative competence of the experimental class was improved by 5.772 points than that of the control class after the experiment.
Copyright (c) 2026 Ying Yin, Wei Li https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence Enabling Civic Education in Colleges and Universities: Synergistic Operation Strategies of Red Culture Dissemination and Enterprises
Under the background of education informatization, the needs of learners are becoming more and more diversified, and how to efficiently allocate educational resources according to the personalized characteristics of learners has become a core issue in the research of college education. In this paper, taking the Civic and Political Education in colleges and universities as the research object, we explored the collaborative operation strategy of red culture dissemination and enterprise based on artificial intelligence. The FKCM algorithm is improved by the algorithm of Approximate Neighborhood Propagation (AP) for the construction of a dynamic portrait of the student population, and a matrix decomposition recommendation algorithm combining reliability and influence propagation is proposed to realize the accurate recommendation of learning resources for Civic and Political Education in colleges and universities. Compared with the standard FCM algorithm and the FKCM algorithm, the AP-FKCM algorithm proposed in this paper both obtain the optimal ACC value. The proposed recommendation model has similar performance with the social recommendation model CUNE, which is better than other comparative models, and has high development value, with RMSE and MAE values of 0.9259 and 0.6315 on 70% training set, respectively.This paper provides a modeling tool for the accurate recommendation of the learning resources of Civic and Political Education in colleges and universities, as well as for the collaborative operation of red culture dissemination and enterprises.
Copyright (c) 2026 Ying Yuan, Rongwang Cheng https://creativecommons.org/licenses/by/4.0/
Analysis of Compliance Issues of Machine Learning Technology in Intellectual Property Protection and Its Legal Regulation
The deepening application of machine learning technology in intellectual property protection makes its compliance gradually become a research hotspot in related fields. This paper uses deep neural network technology to design a digital watermark generation method for intellectual property. And based on the deep neural network watermarking method, the overall system framework of serial number watermarking is constructed. The framework maintains structural features consistent with the neural network output layer through specific matrix operations, thus effectively integrating serial number and deep learning technology. Meanwhile, in order to strengthen the compliance of the deep learning technique, in the generation process of key sample watermarking, the encoder is used to receive normal samples and exclusive signs and output key samples, and the discriminator is utilized to receive normal samples and key samples. As a result, the construction of the digital watermark generation model based on deep neural network is completed. The F1 value of the model is always maintained at 0.7 and above at different Hamming distances, and the maximum is up to 0.973. It shows extremely superior operational performance, which provides a good foundation for its adaptation in intellectual property protection.
Copyright (c) 2026 Zhiqiang Song https://creativecommons.org/licenses/by/4.0/
Research on Student Sports Training Data Modeling and Personalized Training Program Optimization Based on Multi-Layer Perceptro
In this paper, based on the Gray Wolf Optimization (GWO) algorithm, Cauchy variation operator and cosine convergence factor are added, and the convergence speed of the algorithm is enhanced by the position updating formula to shorten the training time, and the improved multilayer perceptron is used for modeling student sports training data. After that, from the perspective of user groups, user-based collaborative filtering algorithm (UB-CF) is selected to model the sportsmen. Then from the perspective of sports, CB recommendation algorithm is used to build recommendation object model based on sports features, and finally UB-CF algorithm and CB algorithm are combined to form a personalized sports recommendation algorithm, so as to achieve the purpose of personalized recommendation to users. The results show that when the overlap rate is 75% and the window size is 3/4 of the original data length, the classification accuracy of the x-y dataset can reach 97.32%. The recommendation effect in the personalized training program of student sports shows that the value of RMSE of this paper's recommendation algorithm (0.1016) is much lower than that of the comparison method, and its predicted value is highly consistent with the actual value. It can be seen that the method in this paper makes the improved recommendation algorithm more perfect and the recommendation accuracy higher by deeply mining the user's preference.
Copyright (c) 2026 Menglong Lin, Wiradee Eakronnarongchai, Jakrin Duangkam, Jinchuan Lin https://creativecommons.org/licenses/by/4.0/
Research on the Innovative Operation Mode of Cross-Strait Forestry Cooperative Enterprises Empowered by the Forest Stamp System--Taking the Transformation of Ecological Product Value as an Example
This study takes the forest ticket system as an entry point to explore its enabling mechanism for ecological product value transformation. A multi-dimensional ecological product value accounting system is constructed, and the SBM-Malmquist model is used to measure the efficiency of ecological product value transformation by combining the 2020-2024 panel data of S City, F Province. The impact of the forest ticket system was assessed based on the double difference model, and the robustness was verified using the placebo test. The results showed that: (1) forest ecological products in the study area have water conservation (70.18%) and carbon sequestration and oxygen release (17.09%) as their core functions, and the spatial distribution of their value is dominated by natural factors, with the strongest explanatory power of the interaction between precipitation and average annual temperature (q=0.83); (2) the efficiency of the transformation of the value of ecological products shows a fluctuating upward trend, and technological progress is the key driving force for the improvement of efficiency and the efficiency value reaches 1.43 in 2023; (3) the forest ticket system has a significant positive effect on the transformation efficiency of ecological product value, and the efficiency is improved by 8.1%-8.2% on average after the implementation in key ecological functional areas, which verifies its effectiveness as an institutional innovation tool.
Copyright (c) 2026 Ruiqian Su https://creativecommons.org/licenses/by/4.0/
Study on the Correlation between Tang Dynasty Literary Creation and Socio-Political Background Based on Big Data Analysis Techniques
Every major change in society has a profound impact on literary creation. The rapid development of big data technology provides new ideas and methods for literary research. In this paper, LDA topic model and K-Means++ text clustering algorithm are used to mine and divide the keywords respectively. The data processing processes such as word division, stop word deletion, and text feature word extraction are performed to analyze the relevance of the text data. The study is based on the database of “Tang Dynasty Literature Chronicle Map Platform” for text mining, and the keywords mined can be roughly divided into six types: ideology and culture, social class, political events, literary style, literary themes, and literary style. Combined with the perplexity index can LDAvis visualization analysis, determine the number of themes is 2 when the difference between the themes is large. Based on Apriori algorithm for association rule analysis, Tang Dynasty literary creation has a strong correlation with political events, social class, ideology and culture.
Copyright (c) 2026 Chi Zhang https://creativecommons.org/licenses/by/4.0/
Optimizing Strategies for Geospatial Perception Development in Early Childhood Science Education Based on Fuzzy Clustering Algorithm
Spatial perception ability is a core component of young children's scientific literacy, involving the coordinated development of multidimensional abilities such as spatial orientation. This study proposes an optimized method for cultivating young children's geospatial perception abilities based on fuzzy clustering algorithms (FCM and FLICM). Combining Piaget's three-stage theory of cognitive development (topological → projective → plane geometry stage), targeted cultivation strategies were designed and group experiments were conducted. The experimental group received strategy intervention, while the control group received traditional teaching. With 217 young children as the research subjects, five ability assessments were conducted: spatial orientation, map cognition, proportional perception, spatial relationship reasoning, and directional description. Fuzzy clustering algorithms were applied to identify six typical ability groups. These included the comprehensive development type (Cluster 1 = 91.48 ± 4.44), map cognition impairment type (Cluster 4 = 74.15 ± 6.13), Comprehensive Delay Type (Cluster 6 = 47.59 ± 7.08), etc. The marginal effect values for MC3: 3D-2D Conversion (17.86%) and SR2: Spatial Pattern Recognition (18.37%) were identified as core weaknesses. After a 30-day teaching experiment, the post-test results showed that the experimental group's total score significantly improved, with a total score of 90.47 ± 9.32, while the control group scored 80.70 ± 10.97, t = 12.438, p = 0.000. The proportion of high-scoring segments (≥85 points) reached 80.6% (87 people), an increase of 43% compared to the control group's 37.6%. The number of children in the low-score segment (≤70 points) decreased sharply to 4 (26 in the control group). This validated that ability diagnosis and strategy optimization based on fuzzy clustering can effectively enhance young children's geospatial perception abilities, particularly for high-scoring groups and key ability shortcomings (MC3, SR2).
Copyright (c) 2026 Debin Si https://creativecommons.org/licenses/by/4.0/
Research and Application of APP Service Design Optimization Based on Multilevel Psychological Needs Analysis
As the multi-level psychological needs of the modern crowd escalate in speed, how to accurately match the multi-level psychological needs of users becomes the focus of APP service design optimization. Firstly, analyze the characteristics of target users through user research to realize the acquisition of user needs, and use KANO model to analyze the importance of the needs of the dominant group of multi-level psychological needs analysis to realize the classification of users' needs. Then, the QFD quality house model is constructed to determine the priority of multi-level psychological needs analysis APP design requirements. Finally, SERVQUAL service quality model is introduced to realize the effectiveness verification of the optimization work of multilevel psychological needs analysis APP service design. The KANO model classifies user requirements into four categories: essential, desired, fascinating and undifferentiated, and the six key indicators identified by the QFD quality house with a stronger order of importance are font size, life application, picture size, anti-fraud, voice input and ease of operation. The comprehensive score of optimization of APP service design for multi-level psychological needs analysis obtained based on SERVQUAL service quality model is 0.759, which meets the user's expectations and provides methodological support for digital psychological services.
Copyright (c) 2026 Fan Zhang, Linfei Yan, Tinghe Wang https://creativecommons.org/licenses/by/4.0/
Copyright (c) 2026 Wenwen Huang, Xiaofang Hu, Fengfei Yang https://creativecommons.org/licenses/by/4.0
Copyright (c) 2026 Shujun Zhang https://creativecommons.org/licenses/by/4.0
Design of Classroom Teaching Model Based on Artificial Intelligence Technology Optimization for Promoting Students' Deep Learning in Convergent Environments
Deep learning is the inevitable point of deepening curriculum reform in Chinese primary and secondary schools and educational change in the age of intelligence, as well as one of the important channels for the development of core literacy. Based on this, the study is based on the development trend of artificial intelligence technology, guided by the theories of online-offline integrated teaching, deep learning and smart classroom, comprehensively adopting quasi-experimental research, independent samples t-test, covariance test and other methods, innovatively constructing a smart classroom (AI-DELC) model that promotes students' deep learning in the environment of online-offline integration, and carrying out the classroom teaching practice with an elementary school in Guangzhou City. The study shows that the AI-DELC teaching model under the online-offline integration environment can effectively enhance students' deep learning effect, implement the core literacy of disciplines, develop students' higher-order thinking, and cultivate students' abilities such as knowledge transfer, problem solving and reflection.
Copyright (c) 2026 Han Xiaoyun, Liang Yingping, Haoyuan Li, Zhengze Li https://creativecommons.org/licenses/by/4.0
The Application Mode and Implementation Path of Smart Housing in Rural Tourism Resource Integration
Since the reform and opening up, China's rural tourism has developed rapidly, and now it has become a new driving force for the growth of the tertiary industry and even the national economy, especially the integration and development of rural tourism and smart housing resources, which further promotes the development of rural tourism and other related industries. In this paper, the coupling role of rural tourism and smart housing is summarized from the four aspects of resources, products, enterprises and markets, and the coupling model is constructed. Using the gray correlation method, the behavioral characteristics of the smart housing-rural tourism system in the study area are reflected, and the final results are calculated through gray correlation to map the size of the degree of association between the data. The coupling coordination degree model was introduced to assess the degree of coordinated development of rural tourism and smart housing with a comprehensive assessment. The more stable indicators of the correlation between smart housing and rural tourism in the study area are the number of domestic tourists and international tourism revenue, with the correlation coefficients of 0.793 and 0.811, respectively. The coupling coordination degree of Haikou and Sanya in the study area in 2023 is 0.609 and 0.582, which are in the state of primary coordination and near-coordination, respectively, and the level of coupling coordination degree of the coastal area is higher than that of other areas. The level of coupling coordination in coastal areas is higher than that in other areas, and relevant countermeasures for the sustainable development of rural tourism are proposed for the dysfunctional situation existing in the study area.
Copyright (c) 2026 Jianfei Xing, Xiao Li, Li Sun https://creativecommons.org/licenses/by/4.0
Energy-saving realization method of commercial space decoration and display design by combining with computer vision
How to achieve energy saving and energy conservation while being aesthetically pleasing and practical is a major challenge that is being faced by the trend of increasing enrichment of commercial space display design. Based on computer vision, this paper utilizes a fisheye camera to obtain a panoramic image of the indoor space environment. Cutting-edge computer image processing technology is chosen to digitize the commercial space elements after edge detection and contour extraction. Through digitizing the spatial images acquired by the fisheye camera, a commercial space environment design model based on computer vision is constructed. At the same time, it discusses the application and performance of form, color and material elements in commercial display space, and realizes energy-saving by complementary matching of elements. The model is used to design the interior decoration and class display of a shopping mall, and analyze the static measurement results, spatial relationship of the shopping mall, goodness-of-fit and regression test results of the design scheme. Among them, the RMSEA of the model design scheme = 0.03 (less than 0.08), and the indicators of GFI, IFI and CFI are all greater than 0.90, which is a good fit. It shows that the model is acceptable for energy-saving design on commercial space.
Copyright (c) 2026 Dandan Zong https://creativecommons.org/licenses/by/4.0
Research on the Intelligent Construction of College Management System under the Diversification of College Students' Education and Management
In this paper, the trajectory data of college students are collected for preprocessing and then populated using the mean trajectory filling algorithm. The student campus trajectory prediction model is constructed to study the activity characteristics of college students in different functional areas. Three features, namely, travel distance, travel mode selection ratio, and pedestrian flow on campus main roads, are extracted to analyze the regularity of trajectory data. The variation of the number of devices in different functional areas is used as the trajectory data of college students, and the prediction model is used to visualize the clustering. 80% of the students' average daily travel distance is less than 12km, and 20% of the students' travel distance is between 12km and 22km. more than 92% of the students choose walking as their travel mode. The foot traffic on each main road has a stronger correlation with the location of their hostel area and the location of their activities. The number of students' equipment has a clear pattern of change in different functional areas. The activities in the teaching area are much higher than the other functional areas in the time period of 6:30-12:30. The frequency distribution of different functional areas in clusters can be used as a basis for predicting the behavioral trajectories of students with different characteristics and realizing intelligent student management in colleges and universities.
Copyright (c) 2026 Xiaojun Wang, Haiyun Zang https://creativecommons.org/licenses/by/4.0
Innovation and Optimization of Red Culture into the Content of Civic and Political Education in Colleges and Universities Empowered by Data Mining Algorithms
In the education of colleges and universities, ideological and political education has become the focus of work, and the integration of red culture in ideological and political education can guide college students to form a correct outlook on life, values and worldview. This paper uses TF-IDF algorithm to analyze the research hotspot, uses Apriori association rule algorithm, principal component analysis method and systematic clustering method to analyze the relevant laws of the red culture integrated into the ideological and political education in colleges and universities, and on the basis of which, it puts forward the content innovation and optimization way of the integration of the red culture into the ideological and political education in colleges and universities. The hot content of this research is curriculum ideology, through the integration of red culture into curriculum ideology, the work of college ideological education can more fully realize its nurturing function and play the role of thought leadership. In the innovation and reform of ideological and political education in colleges and universities, the use of new media on the Internet and the guidance and propaganda of counselors are more important. In addition, the integration of red culture into the ideological and political education in colleges and universities is mainly to excavate the revolutionary culture and the spirit of the times from the ideological and political theory class and course ideology, reform the teaching methods, and incorporate the innovative consciousness, values, patriotism and other ideas embedded in the red culture. This paper focuses on the analysis of the innovation and optimization countermeasures in the integration of red culture into the Civic and Political Education, which has a certain guiding significance for improving the level of Civic and Political work in colleges and universities.
Copyright (c) 2026 Yao Lu https://creativecommons.org/licenses/by/4.0/
Research and Practice on the Application of Artificial Intelligence in the Intelligent Teaching Management Platform of Housing Industry under the Perspective of Resource Sharing
In recent years, artificial intelligence technology, especially deep learning, has been increasingly widely used in the field of education, and housing industry colleges and universities are facing the problems of dispersed teaching resources and low sharing efficiency. In order to improve the intelligent level of teaching management, the construction of an intelligent teaching management platform based on deep learning has become a key direction for the development of education informatization. This study constructs an intelligent teaching resource sharing platform for the housing industry based on deep learning algorithms, realizes personalized learning path recommendation through the LSTM model, and carries out test question recommendation experiments with A, B, and C students as objects. Methodologically, user behavior and basic information are collected, data cleaning and standardization are used to train the LSTM model, and compared and analyzed with traditional recommendation algorithms such as DT and IRT. The results show that in the test of class A students, the F1 value of this paper's method is 0.8502, and the MAE is only 0.0102; the F1 value of class B students is 0.8571; and the F1 value of class C students is 0.8233, which are all better than other algorithms. The conclusion points out that the platform can effectively improve the accuracy and reasonableness of teaching recommendation, especially for students with different learning styles showing good adaptability and generalization value.
Copyright (c) 2026 Yuan You https://creativecommons.org/licenses/by/4.0
The Mechanism of Housing Market Supply-Demand Imbalance on the Financial Performance of Real Estate Firms
In recent years, the imbalance between supply and demand in China's housing market has become increasingly significant, which has a profound impact on the financing environment and financial performance of real estate companies. This paper explores the mechanism of the housing market supply-demand imbalance on the financial performance of real estate companies, taking financing structure as the entry point, and analyzes the data of 72 A-share listed real estate companies for the period of February 2016 to November 2024 using panel regression model. It is found that: endogenous financing is significantly positively correlated with financial performance, with a correlation coefficient of 0.3395; debt financing is significantly negatively correlated with financial performance, with a correlation coefficient of -0.4273; and the correlation coefficient of equity financing is negatively correlated with financial performance at -0.5594. Heterogeneity analysis shows that the impact of financing structure on the financial performance of real estate development enterprises is more significant, and small-scale enterprises are more affected by financing structure than large-scale enterprises. Based on this, this paper puts forward countermeasure suggestions in four aspects, namely, suppressing housing speculative demand, improving the policy of rent and sale, promoting the citizenship of rural migrant workers, and broadening the financing channels of real estate enterprises, in order to optimize the financing structure of real estate enterprises, improve financial performance, and promote the balance between supply and demand in the housing market and the healthy development of the real estate industry.
Copyright (c) 2026 Qingyue Bi https://creativecommons.org/licenses/by/4.0
Financial internal control and final accounts management in higher education: a strategy for optimizing the overall budget performance of government accounting based on the ABC model
With the development of comprehensive budget management, strengthening budget management in colleges and universities is conducive to promoting the scientific and refined management of project funds and improving the efficiency of budget expenditure and management level. This paper introduces the ABC model, realizes the construction of financial budget performance evaluation system of colleges and universities from the construction of evaluation index system and the application of combination empowerment method. Through comprehensive evaluation, it is found that the evaluation score of the budget performance of the sample colleges and universities is 2.96 points, which belongs to the medium level, in which the budget implementation process plays the biggest role in the evaluation results. Following the selection of influencing factors to construct a structural equation model to explore the influencing factors of effective implementation of financial budget performance management in colleges and universities. Administrative support, performance culture, basic resource conditions, performance-related capabilities, and implementation process all positively affect the implementation effect of budget performance management, with the most obvious effect of performance culture (0.874) and administrative support (0.804). The optimization path of comprehensive budget management is explored in depth from multiple dimensions in order to improve the management level of financial budget performance in colleges and universities.
Copyright (c) 2026 Wei Cao https://creativecommons.org/licenses/by/4.0
A study on the design of residential areas incorporating physical education functions - enhancing community health education
Traditional residential area design lacks the integration of physical education functions, making it difficult to meet the health needs of residents. With the enhancement of national health awareness, the integration of physical education functions into residential area design has become an important way to improve community health education. This paper proposes a residential layout optimization design method that integrates physical education functions, which aims to improve the level of community health education. The study extracts the spatial distribution characteristics of the main functional areas of residential areas through deep neural networks, establishes a residential area layout optimization design model, and solves the problem by using the improved adaptive variational particle swarm algorithm (IAMPSO). The experiments are carried out in nine areas of a city and verified under five planning scenarios, and the spatial compactness of urban residential land planned by the research method reaches more than 0.92, which is much higher than that of the planning method based on geospatial information system (0.73) and the planning method based on big data (0.77). Based on the data from the China Household Tracking Survey, the current status of the health level of Chinese residents was analyzed, and it was found that the health score of residents in the eastern region (5.52) was higher than that in the central (5.44) and western regions (5.26), and the health level of urban residents (5.6859) was significantly higher than that of rural residents (5.3143). The study shows that optimizing the layout of residential areas through the IAMPSO algorithm can effectively improve the land space utilization rate, and the integration of physical education functions can improve the level of health education in the community, which provides new ideas for the design of residential areas.
Copyright (c) 2025 Peng Chen https://creativecommons.org/licenses/by/4.0/
Data-driven Study on the Evolution of Cultural Communication Paths in Chinese Language and Literature
In recent years, short videos have jumped to become one of the highly influential communication media. It not only provides a brand-new vision idea for cultural communication, but also gives the possibility of wide dissemination of Chinese language and literature culture. This paper analyzes the types of short video communication modes and communication power mechanism, and draws the technical route of short video production and processing. The communication advantages of Chinese language and literature culture in short videos are divided into three parts: platform advantages, content advantages and technical advantages. Combined with the video recommendation algorithm, a multimodal graph convolutional network algorithm for short video recommendation is proposed. Select experimental datasets, performance indicators, and comparison algorithms for recommendation performance evaluation of multimodal graph convolutional neural network algorithm. Survey questions containing cognitive, value, and behavioral dimensions are designed to capture users' attitudes and perceptions of Chinese language, literature, and cultural communication in short video platforms. Chinese language and literature cultural communication for short video users can deepen users' language and literature cognition. Among them, “I realize the responsibility of inheriting and promoting the culture of Chinese language and literature” has a mean score of 3.98. And "I have learned a lot of cultural knowledge" and "I have revisited Chinese and Chinese Chinese literature" have been highly recognized.
Copyright (c) 2026 Jinming Cui https://creativecommons.org/licenses/by/4.0/
Research on Patriotic Emotional Cultivation and Practical Teaching Mode of Civic and Political Science Courses in Colleges and Universities under the Perspective of Red Classic Architecture
Patriotic emotional education is crucial to the ideological and moral enhancement of college students. This paper utilizes the red classic building to innovate the practical teaching mode of the ideological and political courses in colleges and universities. The construction of the 3-dimensional model of the red classic building is completed through the way of color conferring and mapping. And from the structure optimization and material optimization to improve the model fidelity, improve the students experience immersion. The A-star algorithm is introduced into the 3D campus roaming navigation system of the red classic buildings, and the heuristic estimation function is used to plan the optimal roaming path, which enhances the effect of patriotism inculcation among students. The results show that the A-star algorithm obtains a high adaptability of 2.09 in about 13 iterations, and the path planning effect is better than the comparison algorithm. The experimental class using the 3D campus roaming teaching method of red classic buildings significantly outperforms the control class in the comparison of the three dimensions of patriotism emotion as well as the overall emotion at the level of 0.001.
Copyright (c) 2026 Qin Fan https://creativecommons.org/licenses/by/4.0
Construction of Data Science-based Innovative Talent Cultivation Path in the Integration of Civic and Political Education and Dual Innovation Education in Colleges and Universities
Civic-political integration of dual-creative education of is the strategic thinking of the training of innovative talents in data science. It is closely related to the cultivation of students' entrepreneurial ability and innovation spirit, this paper identifies the influencing factors of Civic and Political Integration of Dual Innovation Talent Cultivation through the Deferral method, and constructs the index system. Using the DEMATEL-ISM method, the influence matrix is established and the center degree and cause degree are calculated. Draw a multi-layer recursive order structure diagram to determine the hierarchical relationship between the influencing factors. Finally, combining the MICMAC method, the dependency and driving force of each influencing factor are obtained. It provides a theoretical basis for proposing an effective integration path of Civics and dual-creation education. The results show that 15 influencing factors are refined and generalized. Among them, the centrality of innovation motivation (16.552), scientific research and innovation ability (16.211), and local government education expenditure (16.024) ranked high. The influence degree value of the effect of Civic Spirit Guidance ranks first and can influence other factors. Market environment, innovation contest results, and local government education expenditure are the driving force and highly dependent correlates. In summary, 3 aspects of cultivation paths are finally proposed: policy suggestions, diversified environment and culture construction, and multidimensional innovative activities and competitions
Copyright (c) 2026 Xiaoli Feng https://creativecommons.org/licenses/by/4.0
Research on the Path of Integrating Craftsmanship into Mental Health Education in Colleges and Universities under the Concept of Three-Whole Parenting Based on Artificial Intelligence Technology
This project is centered on the two aspects of the concept of education and service effectiveness to carry out the work of artificial intelligence technology-enabled three-pronged education. Based on the linkage between craftsmanship and mental health education, a corresponding educational program is designed. Taking the research data as the entry point for validation and analysis, we use mathematical statistics to prove the practical application of the educational methods in this paper. Before the intervention, there is no significant difference between the two groups of students in the dimensions of the effectiveness of mental health education (p>0.05), that is, the research sample is effective. After a period of experimental intervention on the research sample, the two groups of students, in all dimensions, showed significant differences, which verified that the educational program of this paper has a good practical pedagogical effect and added three strategies for the development of mental health education.
Copyright (c) 2026 Yan He https://creativecommons.org/licenses/by/4.0
Research on the Accuracy Path of College Students' Civic and Political Education Work Based on Deep Learning in the New Media Era
In the face of massive ideological and political education resources and textual information, how to quickly and accurately obtain ideological and political content that meets the teaching needs is an urgent problem to be solved. In this regard, this paper constructs an information extraction dataset in the field of curriculum Civics and politics, and proposes a new NCFCA algorithm based on the neural collaborative filtering algorithm of channel attention. Simulation test and coverage test of the system designed by NCFCA are carried out in the constructed information dataset of Civics and Politics, and comparative experiments are conducted with different recommendation systems. And the NCFCA method oriented to learning resources recommendation is applied to the actual Civics teaching. The results show that the average MAE value of the five groups of experiments of the system under the NCFCA algorithm is 0.606, and the F1 value is 0.306. The coverage rate of the automatic recommendation method for college students' Civic and political teaching resources based on the NCFCA model when recommending Civic and political education and teaching resources for the users is 98.4, which is superior to the performance of the system under the other three algorithms, indicating that the NCFCA system has the highest coverage rate of the resources and the best recommendation effect. . Nearly 90% of the students think that the NCFCA method is very accurate for recommending learning ideological and political resources, and in test scores 3 and 4, the average scores of the students in the experimental group and the control group are 86.54, 86.18 and 77.40, 77.47, respectively, which is a large difference between the two, indicating that the NCFCA method can effectively improve the learning effect of the students. This study fully demonstrates that the NCFCA method proposed in the article plays an important role in promoting the precision of college students' civic education and has strong feasibility and effectiveness.
Copyright (c) 2026 Xiaoxing Hou https://creativecommons.org/licenses/by/4.0
Intelligent Integration of Civic and Political Elements in Physical Education Teaching and Design of Personalized Learning Paths
Based on the application value of Civics and Politics elements in physical education teaching, this paper constructs a personalized management framework that includes learning state feature analysis, learning effectiveness prediction, teaching intervention decision-making and other parts, and utilizes the techniques of reinforcement learning algorithms and Q-functions to study students' behaviors and assist teachers in decision-making. The Civics element is integrated into the personalized management framework of physical education teaching, and the effectiveness of the method in improving students' Civics cognition, physical fitness, and mental health is studied through comparative experiments. In the comparison of the level of civic and political cognition, the P-values of seven indicators such as national self-confidence and three indicators such as the spirit of striving are less than 0.05 after the experiment, which proves that the approach of this paper can significantly improve the level of students' civic and political cognition. In the comparison of physical condition, the P-value of 6 indicators such as 50-meter running after the experiment is less than 0.05, and the physical quality of the students in the experimental class is significantly better than that of the control class, which proves that this paper's approach can effectively improve the physical quality of the students. In the comparison of mental health, the P-value of 5 indicators such as depression factor after the experiment is less than 0.05, and some indicators are less than 0.01, and the mental health of the experimental class is significantly better than that of the control class, and the learning path of this paper also has a certain role in promoting students' mental health.
Copyright (c) 2026 Quan Luo, Rongrong Hu https://creativecommons.org/licenses/by/4.0/
Optimization of Intangible Cultural Heritage Art Education and Inheritance Paths Assisted by Artificial Intelligence
Nowadays, digital information technology has made outstanding achievements in the protection of many large-scale tangible heritage, “digital protection of intangible cultural heritage” is gradually known and used by people, and the data has become a new valuable resource. This paper firstly analyzes the influencing factors of the inheritance of intangible cultural heritage art, and obtains the final fitting results of the logistic regression model. It optimizes the curriculum objectives, curriculum content, and teaching methods of art education, and proposes the reform of art education. Taking Shaanxi folk songs as an example, a survey experiment was designed to analyze the respondents' evaluation of Shaanxi folk songs, and most of the respondents believed that the inheritance and development of folk songs needed to be intervened, and the support rate of "inheritor training" was the highest, with an average score of 5.26, followed by "documentary record" and "nationwide promotion", with a score of 5.24. The students were guided to the teaching practice of non-heritage music inheritance, and their artistic performance and cognition were analyzed through the control test. The mean values of artistic performance and cognitive assessment of the students in the experimental class are 7.984 points and 8.208 points respectively, which are better than those of the control group, indicating that the teaching practice of the experimental class is more efficient and the non-heritage culture can be better inherited and educated.
Copyright (c) 2026 Jie Huang https://creativecommons.org/licenses/by/4.0
Research on the Innovation and Inheritance of Traditional Cultural Elements in the Environmental Art Design of Modern Housing
The value of traditional culture in modern residential environment design has gradually been paid attention to, and housing art design integrating traditional cultural elements helps to enhance residential aesthetics and cultural identity. This study centers on the innovative application of traditional cultural elements in modern housing environment art design, and selects 10 new Chinese-style residential districts in City D as samples to be analyzed through on-site research, SD evaluation method and principal component analysis. The survey used 16 landscape evaluation factors and invited 45 evaluators with different professional backgrounds to complete the questionnaire. The results showed that cleanliness (0.86) and openness (0.74) had the highest scores, demonstrating the advantages of the current new Chinese design in terms of space and hygiene. The results of the regression model showed that symmetry (0.635), landscape vignettes (0.617) and infrastructure (0.469) were the key factors to enhance landscape quality. The explanatory power of the model was good (R² = 0.815, Sig < 0.05). The conclusion points out that the effective integration of traditional cultural elements needs to focus on the optimization of specific visualization elements, and combine modern technology and regional cultural context to promote the coordinated development of cultural heritage and modern design.
Copyright (c) 2026 Qingqing Huo https://creativecommons.org/licenses/by/4.0
Privacy Protection Mechanism and Residential Security Enhancement Countermeasures for Smart Homes Combined with Internet of Things Technology
As an important application scenario of the Internet of Things (IoT) technology, smart homes realize the interconnection and intelligent management of home devices through sensors, RFID chips and other devices. However, smart home devices face serious privacy leakage risks during data collection, transmission and processing, and sensitive data such as user behavioral data and home environment information are easily acquired and exploited by malicious attackers. In this study, a smart home privacy protection mechanism based on federated learning and differential privacy is proposed for the privacy protection of IoT devices in smart homes. The methodology adopts an adaptive hierarchical differential privacy adding noise algorithm to quantify the layer contribution by calculating the non-zero percentage of activation values and the amount of gradient change in each layer to realize the dynamic privacy budget allocation. Meanwhile, the wireless federated learning system model is established to characterize the channel properties between the base station and the user equipment using the block fading model, and combines the Gaussian and Laplace mechanisms to provide differential privacy protection. The experimental results show that on the MNIST dataset, the performance of this paper's algorithm reaches 95.16%, which is 3.56% higher than the competitive algorithm AUTO-S when the privacy budget takes the value of 10. In the smart home device recognition task, the method achieves an average adversarial rate of 98.625% in the white-box scenario and 89.39% in the black-box scenario. The conclusion shows that the privacy-preserving mechanism can effectively protect user privacy while ensuring the usability of the model, which provides reliable security for the smart home system and has good practicality and popularization value.
Copyright (c) 2026 He Jiang, Xiaoru Li https://creativecommons.org/licenses/by/4.0
Research on Virtual Reality Scene Construction and Space Layout Optimization in Interior Design Based on Collaborative Optimization Algorithm
With the wide application of virtual reality technology to the field of interior design, people pay more and more attention to the spatial layout of virtual interior scenes and the simulation effect of scene construction. In this study, we first design the indoor spatial point matching algorithm based on three-dimensional vision, use the similarity metric formula to solve the distance between the spatial points in the base image and the original image, and eliminate the wrong points to improve the spatial point matching accuracy, and then use the VR technology to carry out the virtual modeling of the interior scene. The energy function is introduced for optimization, spatial layout planning is carried out, and the collaborative particle swarm algorithm is used for optimization to realize the exploration of indoor virtual reality scene construction and spatial layout optimization methods. In the chi-square test p-value, for Q1 (the null hypothesis is that participants vote the same number of votes for each emotion), the p-value is much lower than 0.05, and the results indicate that there is a difference in voting for each emotion. For Q2 (the null hypothesis is that the participants vote the same number of votes for the answer to the question of whether the color distribution is similar to the common color distribution of that type of scene in other scenes except the real scene), the results are more diverse, which shows that the color distribution is similar to the common color distribution of that type of scene, thus verifying the validity and correctness of the method in this paper.
Copyright (c) 2026 Jiefang Jin https://creativecommons.org/licenses/by/4.0
Research on Cybersecurity Threat Prediction and Prevention Technology for Intelligent Housing Combined with Edge Computing
As an important application scenario for the development of Internet of Things (IoT) technology, smart housing is facing unprecedented challenges in its network security. The traditional centralized security protection architecture in the face of large-scale distributed smart devices have problems such as high latency, slow response, etc., and it is difficult to meet the real-time threat detection and protection needs. Aiming at the problems of insufficient real-time threat detection and limited protection effect of smart housing network security, this paper proposes a smart housing network security threat prediction and prevention technology combined with edge computing. First of all, the distributed security monitoring architecture based on edge computing nodes is constructed, the K-means clustering algorithm is used for network traffic anomaly detection, the support vector machine is used to achieve threat intrusion identification, and the dynamic heterogeneous redundancy adaptive defense mechanism is established; then, the multilayer threat detection model is designed, and the behavioral anomaly analysis and threat prediction are combined with machine learning algorithms; finally, the edge infrastructure is secured and trusted through the edge, data security and network security three-layer protection system to build a complete security prevention framework. The experimental results show that the data processing rate of the system reaches 2350 items/s, the mean value of threat detection error is 0.396, the monthly average defense rate reaches 96.262%, and the path interception probability is as low as 0.935.The research results validate the effectiveness of the edge computing technology in the prediction and prevention of network security threats in smart housing, and provide new technical paths for the construction of efficient real-time security protection system of smart housing. The results verify the effectiveness of edge computing technology in the prediction and prevention of smart housing network security threats, and provide a new technical path for building an efficient real-time smart housing security protection system.
Copyright (c) 2026 Guo li, Minghua Wang https://creativecommons.org/licenses/by/4.0
Sustainable Application and Economic Efficiency Analysis of Ship Transportation in the Transportation of Residential Building Materials
As a critical link in the supply chain for residential building materials, the sustainability and economic efficiency of shipping play a key role in the low-carbon transformation of the construction industry. This paper establishes a sustainability optimization framework for shipping based on big data analysis and intelligent algorithms, covering key technologies such as ship design and the development of intelligent vessels. A hybrid optimization algorithm combining genetic algorithms and nonlinear programming is proposed to optimize the ship hull form with wave resistance Rw as the objective function. By leveraging augmented reality for real-time, all-around navigation assistance, the study achieves intelligent shipping operations. Using net present value and internal rate of return models, the economic benefits of different transportation scenarios are quantified. Case analyses demonstrate that the optimized ship design reduces resistance by 4.96%, the intelligent navigation system improves visual communication efficiency by 14.29%, and the comprehensive solution achieves over 25% higher economic benefits compared to the baseline.
Copyright (c) 2026 Hongwei Li, Qiaozi Gao https://creativecommons.org/licenses/by/4.0
Design and Practice of English Learning Support Systems in Intelligent Housing Environments: Exploring the Integration of Technology and Education
In order to enhance the effectiveness of classroom teaching, this paper designs and builds a set of English learning support system by combining an intelligent housing environment based on multimedia technology with English teaching. The system architecture mainly includes four functional layers. The data layer relies on the collaboration of multiple servers to realize the transmission and preservation of data. The algorithm layer adopts deep learning algorithms to complete semantic understanding and classification of utterances. The intermediate logic layer accomplishes speech recognition and intelligent dialog through the AI module, the script module is used to process the business logic in the speech data, and the image module recognizes and simulates residential scenes, characters, actions and expressions. The performance layer is equivalent to the client, supporting operations such as system login, system settings and logging out. The experimental results show that the system takes less than 30s to respond to user requests such as questions, corrections, pre-class pre-study and post-class consolidation, and the accuracy of the system in intent recognition and slot value classification of English semantics is more than 95%. This shows that the system's stable performance and high semantic recognition accuracy in the intelligent housing environment can effectively improve students' English learning efficiency in daily life scenarios.
Copyright (c) 2026 Peilan Peng https://creativecommons.org/licenses/by/4.0/
A corporate bankruptcy early warning framework based on principal component analysis to optimize the construction of financial index system in big data environment
If we can find and take effective measures when the financial situation of the enterprise is just in crisis, we can make many enterprises avoid suffering greater losses and bankruptcy. This paper uses principal component analysis to construct and standardize the data matrix of enterprise financial early warning indicators, optimizes the indicator system and inputs it into the BP neural network model, and determines the risk of enterprise bankruptcy early warning according to the output of the model. The research results show that the financial early warning indicators selected in this paper can significantly distinguish between enterprises with and without bankruptcy risk, and the average accuracy of the model in early warning of enterprise bankruptcy risk is 92.5%. At the same time, the validity and reliability of the model are found to be high in individual cases. The model in this paper has good predictive ability, has the significance of guiding practice, and the results of the study well support the effectiveness of principal component analysis in the enterprise bankruptcy early warning model.
Copyright (c) 2026 Mingwei Li https://creativecommons.org/licenses/by/4.0
Innovative Research on Rural Housing Renewal and Rehabilitation Models Driven by Rural Electricity Economy
Under the background of rapid development of e-commerce economy, rural housing renovation faces new opportunities and challenges. The traditional housing renovation model is difficult to meet the needs of modern rural development, with obvious spatial differentiation characteristics and complex and diverse influencing factors. Exploring the innovative model of housing renovation driven by e-commerce economy is of great significance for promoting rural revitalization, improving rural living environment, and promoting the coordinated development of urban and rural areas. This study adopts a combination of geographically weighted regression (GWR) model and random forest (RF) model to deeply analyze the spatial influencing factors and threshold effects of rural housing renovation driven by e-commerce economy, based on 800 rural housing renovation sample sites in County A. The spatial regression analysis was carried out by constructing the characteristic price model, using ArcMap 10.3 software, and producing partial dependency maps using Python 3.0 software. The results show that: the goodness-of-fit R² of the GWR model reaches 0.8314, which is significantly better than that of the OLS model of 0.7963, and the standard deviation decreases from 0.623 to 0.436; the prediction accuracy of the RF model is as high as 94.1%, and the average absolute error is 996.52 yuan/square meter; the housing location has the greatest influence on the remodeling price, with the contribution of 20.21%, followed by the transportation accessibility (16.32%) and park accessibility (13.63%); spatial autocorrelation analysis shows that the Moran's I value increases from 0.286 to 0.333 in 2021-2024, showing a significant positive correlation clustering characteristics. The study puts forward three countermeasure suggestions to improve the design of housing system, pay attention to the orientation of social opinion, and enhance the economic strength of farmers, which provide theoretical basis and practical guidance for the innovation of rural housing renovation mode.
Copyright (c) 2026 Rong Li, Chaonan Liu, Bei Li https://creativecommons.org/licenses/by/4.0
Optimization research on automatic control and servo control system of high-precision assembly industrial robot based on variational method
The development of industrial robotics technology has led to a large number of applications in assembly tasks in production practice, but due to factors such as the complexity of the assembly process, the robot's own positioning accuracy and the limitations of vision technology, there are still great challenges in the control of high-precision assembly of robots. In this paper, a high-precision assembly industrial robot automatic control and servo control system combined with flexible assembly technology is proposed. In this system, an actuation system and a mechanics perception system are designed. The actuation system is based on 6PUS-UPU parallel robots, and the motion control of the assembly robot is realized by kinematic inverse solution. The mechanics sensing system is constructed based on a six-dimensional force sensor as a way to obtain the force/torque data during the assembly process of the industrial robot. Then the robot motion model is linearized using the variational method, and then an ideal impedance model is set for the adjustable parametric impedance control system to realize the supple control of the industrial robot with reference to the adaptive control method. It is found that the optimized automatic control and servo control system of the high-precision assembly industrial robot in this paper has higher assembly efficiency and strong fault-tolerant assembly effect. Therefore, the optimization of the automatic control and servo control system of high-precision assembly industrial robots can lay a solid foundation for improving the industrial production level.
Copyright (c) 2026 Yan Li https://creativecommons.org/licenses/by/4.0
The role of home classroom climate on motivation in elementary school students based on residential space optimization
In recent years, the demand for home-based learning has grown, and the importance of optimizing residential spaces to create a positive learning atmosphere has become increasingly important. This study investigated the effects of home classroom atmosphere on elementary school students' motivation and academic immersion. A questionnaire survey was used to investigate 1,287 elementary school students in Hefei, Nanjing, and Shanghai. The results showed that home classroom atmosphere was significantly related to academic motivation (r=0.383, P<0.01) and home classroom atmosphere was significantly related to academic immersion (r=0.565, P<0.01). Regression analysis showed that home classroom climate and academic immersion explained 38.13% of the variance in academic motivation. Bootstrap analysis confirmed that academic motivation mediated the relationship between home classroom climate and academic immersion (95% confidence interval: 0.066-0.178). The study found that optimizing residential space to create a positive home classroom climate can effectively enhance elementary school students' motivation and academic immersion, providing theoretical support for home education practice.
Copyright (c) 2026 Yang Li https://creativecommons.org/licenses/by/4.0
Data-driven Teaching Mode Innovation and Teaching Quality Improvement Strategies for English Education in Colleges and Universities
Combining the data era with the characteristics of teachers and students to innovate the teaching mode is an effective means to improve the quality of English education and teaching in colleges and universities. In this paper, under the guidance of modern teaching theory, in-depth study of the operation principle of the expert system in the field of artificial intelligence, to categorize and summarize the English knowledge points with its knowledge representation, and thus build the English teaching expert system in this paper. In the actual English teaching in a university, the experimental class A and the control class B, which have no significant difference in total scores, are selected to carry out the comparative experiments on the auxiliary effect of this expert system. The average value of a number of indexes of the experimental class that used the English teaching expert system of this paper changed a lot, and the degree of improvement was very significant (P<0.01 or P<0.05), which indicates that the English teaching expert system of this paper can effectively assist the English teaching of teachers and the English learning of students in colleges and universities, and improve the quality of English teaching while updating the form of education.
Copyright (c) 2026 Yingping Liang, Han Xiaoyun https://creativecommons.org/licenses/by/4.0
The Path of a Computer-Supported Collaborative Learning Approach Based on Data Mining Techniques to Promote Students' Intercultural Communication Competence in the English Language Discipline
Computer-supported collaborative learning (CSCL) methodology can facilitate communication among learners in online learning platforms. This study proposes a practical strategy for English majors to apply CSCL teaching model and students' cross-cultural communication competence cultivation. By mining the characteristics of students' learning behaviors, combined with an improved density clustering algorithm, collaborative learning cluster construction is realized. Finally, practice teaching is carried out to verify its effectiveness from the improvement of students' intercultural communication ability. Through the paired-sample test of the knowledge dimension of the experimental class and the control class, it is found that the paired-sample t-test results of the pre and post-tests of the knowledge dimension of the experimental class show that T=-18.605, P=0.000, and the P-value is lower than the significance of 0.05, which can be obtained that there is a significant difference between the performance of the pre and post-tests of the experimental class before and after the experiment, and that the post-tests of the experimental class are superior to the pre-tests, which leads to the conclusion that based on the The computer-supported writing learning method based on data mining technology plays a positive role in the improvement of students' intercultural communication skills.
Copyright (c) 2026 Chang Liu https://creativecommons.org/licenses/by/4.0
Optimizing the Content Generation Mechanism of Cross-Cultural Brand Communication Using Web Algorithms in Converged Media Environment
In order to enhance the effectiveness of cross-cultural brand communication, this paper proposes the Transformer model using Self-Attention mechanism and Multi-Head Attention mechanism for content generation. Combined with the pointer network, the TP model based on the optimized Transformer model is designed. Conduct ROUGE score comparison experiments between the TP model and five mainstream copywriting generation models to evaluate their performance effects. To further validate the usability of the content generated by the TP model, this paper uses a questionnaire to analyze the audience's agreement. The results show that the performance comparison between the TP model and the AAT model, which has the best performance among the five comparison models, increases the scores in the ROUGE-1, ROUGE-2, and ROUGE-L evaluation functions by 0.00982, 0.00143, and 0.00953 in that order. The average emotional resonance identity, average value confirmation identity, and average sharing and Diffusion recognition were 3.9304, 3.9806, and 4.0202, respectively, indicating that the use of the TP model to generate marketing copy can trigger audience resonance and brand recognition, providing new ideas and methods for brand cross-cultural communication.
Copyright (c) 2026 Bing shen https://creativecommons.org/licenses/by/4.0/
Deep learning-based dynamic monitoring technology for soil moisture in upland turf stripping
In this paper, the inversion of soil moisture in highland turf stripping was carried out by constructing an artificial neural network model, improving the vegetation-adjusted vertical drought index (VAPDI), and processing CYGNSS data. The accuracy of the inversion model was verified by linearly fitting the VAPDI calculated from remote sensing images in the observation area and the measured soil moisture data in the same period in SPSS. More accurate reflectance was obtained by surface roughness attenuation correction. After integrating the topographic feature quantity, the accuracy of the model inversion situation was analyzed by comparing the index situation of the artificial neural network model with other models. The linear regression model for a total of three periods of data from 2020-2022 in the observation area passed the significance test of 0.05. The interpolated Bias, RMSE, and R values of the artificial neural network model were better than those of the other comparative models, and the training time was 30% and 15% less than that of the other models. The model inversion results were consistent with the actual soil moisture situation with high accuracy.
Copyright (c) 2026 Yan Liu, Ji Wang, Ming Zhao https://creativecommons.org/licenses/by/4.0
Research on three-dimensional animation character hair rendering and virtual scene fusion technology based on ray tracing algorithm
The increasingly powerful performance of computational graphics chips, the rise of augmented reality and virtual reality, real-time graphics rendering has a pivotal role in today's social life, and realistic hair rendering has been one of the difficult problems in computer graphics. In this paper, based on the changes of viewpoints, objects, and light sources in the 3D scene changes, we propose ray tracing model and calculate its pixel unbiasedness to ensure the correctness of the calculation results. A simplified Fresnel formula is used to calculate the contour light of hairs, and at the same time, an improvement of the basic Fresnel formula is proposed to enhance the calculation of the table, contour light intensity and width. Based on the principle of hierarchical shadow mapping, realize the simulation of hair sub-shadow effect. Construct the virtual scene, obtain the body skeleton and other data of the 3D animated character, and improve the fusion between the virtual scene and the 3D animated character. After analyzing the rendering performance and the fusion effect of the virtual scene, after turning on the simulation module, the GPU time increases by 16.98%, while the CPU time increases by 25 μs, and the system's overhead of the Compute Shader reaches 18.65%. The algorithm seeks the mean image in 40-60 images, and the 2×2 chunk fitting method can get better experimental results, at this time, the PSNR is 38.7651dB and 38.8879dB, respectively.
Copyright (c) 2026 Yuxuan Liu https://creativecommons.org/licenses/by/4.0
Modeling Multicultural Interaction in Silk Road Ethnomusicology Education Based on Deep Learning Framework
The countries along the Silk Road are rich in music resources, and the unique multicultural interaction of ethnic music education is an important trend to carry out the living heritage and innovative development of ethnic music culture. In this paper, based on analyzing the dynamic interaction between technology and ethnic music culture, a Trans-GAN model is constructed by combining Transformer Network and GAN model to generate diversified ethnic music. Using the generated diversified ethnic music as teaching resources, we established an ethnic music teaching model by combining the concept of realm pulse. A teaching experiment was designed with the teaching model to verify the feasibility of the model to enhance the multicultural interaction level of ethnic music with the students majoring in ethnic music in G Nationality University as the research object. The results show that the deep learning model can generate more diversified ethnic music of the Silk Road, and the students' ethnic music performance and cultural interaction level can be significantly improved. The integration of deep learning technology into folk music education to realize the diversification of teaching resources provides technical support to help students enhance the level of multicultural interaction, and also provides a new path for the inheritance and innovation of Silk Road folk music.
Copyright (c) 2026 Xue Zhao, Dan Shen https://creativecommons.org/licenses/by/4.0/
Quantitative Analysis of the Impact of Optimized Design of Urban Negative Space Living Environment on Community Vitality Based on Spatial Syntax Theory
In order to obtain intuitive and quantitative analysis results, this paper chooses spatial syntax theory as the main basic theory of the study. Through field research in three typical negative spaces in linear cities around residential stations, we obtain the characteristics of pedestrian behavior and public needs, apply spatial syntax variables to quantitatively analyze the urban residential environment, explore the problems of urban public spaces and propose optimization strategies. Taking the spatial characteristics of the residential environment as the independent variable and the spatial vitality as the dependent variable, a model of the overall vitality influencing factors of the linear urban public space around the residential sites is established to obtain the degree of influence of each spatial carrier element on the vitality of the community. Analyze the optimized urban residential space based on spatial syntax as well as community vitality. The optimized urban residential environment has a high degree of integration and accessibility and a smooth road system. The spatial design is adjusted using spatial syntax to include the internal road network of open parks, in which the growth rates of the vitality parameter values of Longjiang Street, Hohsing Third Avenue Street, and Hengdao Street, for example, are 81.09%, 92.38%, and 73.40%, respectively, and the values of the vitality parameters of these streets are improved significantly.
Copyright (c) 2026 Lushan Shi https://creativecommons.org/licenses/by/4.0/
Research on the Pathways for the Digital Economy to Drive the Modernization of the Housing Industry Chain and Supply Chain
Under the impetus of the digital economy, the digital transformation of industrial chains and supply chains has become inevitable. Data assets effectively enhance the operational efficiency of industrial chains and supply chains, digital technologies can effectively enhance the competitiveness of enterprises in industrial chains and supply chains, and digital technologies effectively promote the transformation and upgrading of industrial chains and supply chains. This paper constructs an evaluation indicator system from four dimensions: research and design modernization, production and manufacturing modernization, logistics and transportation modernization, and sales and marketing modernization. Based on data from 30 provinces in China from 2015 to 2024, the paper uses the entropy method and spatial Moran index to analyze the digital economic development levels and spatial correlations of the 30 provinces, and examines the impact of the digital economy on the modernization level of the housing industrial chain and supply chain, as well as its underlying mechanisms. The research results show that the development of the digital economy exhibits a stable growth trend, while the modernization level of the housing industry chain and supply chain shows an overall fluctuating upward trend, with the overall modernization level of the housing industry chain and supply chain first declining and then rising. Additionally, the promotional effect of the digital economy exhibits significant spatial heterogeneity, with the development level of the digital economy generally showing a single threshold effect on the modernization level of the housing industry chain and supply chain, and a double threshold effect in western regions.
Copyright (c) 2026 Hanqi Song https://creativecommons.org/licenses/by/4.0/
The Construction of Information Flow Delivery and Reader Interpretation Mechanism of English and American Literary Works Based on Semantic Network Analysis
This paper demonstrates the information flow transmission process of English and American literary works through two dimensions: lexical and discourse, and explores the discourse information reception from the reader's perspective. Taking the classic Anglo-American literature A Tale of Two Cities as the research object, relying on the lexical analysis technology to count the high-frequency words in the work, and the semantic network technology to conduct co-word analysis. Graph Attention Network GAT is utilized for relevance retrieval to construct a mechanism for readers' interpretation of English and American literature. The results show that Alexander Manette has the highest frequency of occurrence, reaching 1,901 times. Its pointwise centrality and middle centrality were the largest, 1476.000 and 503.062, respectively, and its proximity centrality was the smallest, 48.000. The highest degree of connection with “Alexander Manette” is “Charles Darnay”, with a total of 157 occurrences. Analyzing the information transfer characteristics of the discourse from the perspective of author's information processing and introducing semantic networks for reader interpretation analysis provide new ideas and methods for the dissemination of English and American literature.
Copyright (c) 2026 Dan Wan https://creativecommons.org/licenses/by/4.0/
Research on Innovation and Entrepreneurship Opportunity Identification and Risk Avoidance in Digital Transformation of Housing Industry Chain
Against the background of accelerated digital transformation of the housing industry chain, innovation and entrepreneurship have become an important engine to promote industrial structure upgrading and economic growth. Identifying entrepreneurial opportunities and effectively avoiding entrepreneurial risks have become the core issues of concern for policy makers and entrepreneurs. This study focuses on the identification of innovation and entrepreneurship opportunities and risk avoidance in the context of digital transformation of the housing industry chain, and adopts the fuzzy set qualitative comparative analysis (fsQCA) method and the DEMATEL-ISM model to conduct a systematic analysis. 492 valid samples were collected through questionnaires, and variable calibration was completed after reliability and validity tests. The results of the fsQCA indicate that entrepreneurial alertness, learning ability, social network and educational environment constitute a “two-factor entrepreneur-environment driven” path; Digital technology, policy and education conditions constitute the “all-factor-driven” path, and three high entrepreneurial opportunity identification groupings were identified, with an overall consistency of 0.841 and a coverage of 0.426. In terms of risk aversion, the DEMATEL-ISM model identifies the influence of financial support and government support as 0.6983 and 0.6918 respectively, which are core risk factors. The study reveals the entrepreneurial opportunity generation mechanism and entrepreneurial risk hierarchy relationship under multiple concurrent conditions, which provides a basis for entrepreneurial decision-making in the digital upgrading of the housing industry chain.
Copyright (c) 2026 Yuqi Wu, Hanjing Wu https://creativecommons.org/licenses/by/4.0/
Construction and Effectiveness Evaluation of an Artificial Intelligence-Assisted Student Affairs Processing Model for Counselors
This paper argues that the role of college counselors in the era of artificial intelligence is undergoing a profound transformation, and they not only need to have a solid theoretical foundation in education and practical experience, but also need to continuously improve their technological literacy and data analysis ability to adapt to the needs of the new era. At the same time, college counselors also need to actively innovate the student affairs processing mode, combined with the development trend and application prospects of AI technology, to explore a new path of student affairs processing with high efficiency and practicability. Specifically, big data, artificial intelligence and other technologies can be embedded in the student management system of colleges and universities, and design a set of intelligent transaction processing system for counselors to assist counselors to improve work efficiency. Finally, this project has designed and realized the intelligent transaction processing system, which has basically met the needs of college counselors to deal with student affairs. Through practical application, it is found that the system is effective in improving the work efficiency and quality of college counselors.
Copyright (c) 2026 Hongdan Xiao, Ping An, Guangwei Zhang https://creativecommons.org/licenses/by/4.0/
Research on Artificial Intelligence Application Strategies in the Integration of Physical Education Teaching and Athletic Training for College Students in Higher Education
In this paper, the hourglass network is used to extract the key points of human skeleton of students during physical education teaching and sports training, and to estimate the students' single movement posture characteristics by eliminating the abnormal frames and coordinate transformations, parameter calculations, and division of possible movement intervals. Based on the differences in the movement posture features, the DTW algorithm is used to calculate the similarity between the student's movements and the teacher's movements during physical education and sports training, and score them accordingly. In the key action posture matching, the matching success rate of this paper's method for six types of actions is between 70.52% and 90.43%, which is higher than that of other methods. The classification accuracy calculated by this paper's method based on similarity is high, and the number of classification errors for the 6 classes of actions is mostly in the range of 0-3 times. The number of model parameters of this paper's method on 2 validation sets is only 13.1M, which is lightweight. In the motion pose matching deviation rate experiments, the scoring deviation rate of this paper's method for the excellent and unqualified phases ranges from 3.16% to 5.56%, and the overall scoring deviation is in line with expectations.
Copyright (c) 2026 Shiyu Xie https://creativecommons.org/licenses/by/4.0/
A Study on Analyzing Students' Psychological Characteristics and Learning Styles Using Clustering Algorithms
Learning styles are also known as learning preferences or the way learners prefer to learn. Each person has his or her own characteristics and ways of dealing with attitudes, processing and perception of problems. The study collects data by distributing questionnaires to primary and secondary school students in a city, including the collection of data related to students' psychological characteristics and students' academic performance, and preprocesses the data. Subsequently, on the basis of the traditional K-means algorithm, a data analysis method of students' psychological characteristics based on the K-means clustering algorithm improved by the genetic algorithm is proposed to explore the association between students' psychological characteristics and learning styles. The results show that the K-means clustering algorithm based on genetic algorithm significantly improves the clustering speed. According to the clustering results, the students' psychological characteristics were categorized into 4 classes, and the anxiety type had the highest percentage (47.54%), and its scores were lower. There are different learning style preferences for different students' psychological characteristics, and teachers should use learning styles as a reference to design targeted teaching activities so as to improve the teaching effect.
Copyright (c) 2026 Tian Xu, Shige Wang https://creativecommons.org/licenses/by/4.0/
The Fabric Narrative of “Home”: The Local Expression of Women's Red Art in Community Public Buildings
As an important form of traditional Chinese women's handicraft art, women's red carries rich cultural significance. In modern context, its regeneration and expression in public space, especially through the fabric narrative into the community architectural environment, has become an important medium connecting historical memory and contemporary expression, with the dual value of cultural heritage and spatial identity. This paper explores the path of local expression of women's red art in community public buildings through digital image processing and color analysis. The study firstly collects and pre-processes 152 images of women's red fabrics in different periods, extracts representative colors through K-means clustering algorithm combined with PCCS color system, and further analyzes the distribution of hue, brightness and saturation. The results show that the colors of women's red fabrics are dominated by red and blue, accounting for 50.16% and 30.82%, respectively; the brightness is concentrated in the middle and high range, accounting for 93.42%; and the strong hue is dominated, accounting for 27.41%. On this basis, the evaluation system of community public buildings' locality composed of 24 indicators was constructed, and the AHP method was used to complete the indicator assignment and validity test. 268 valid questionnaires were recovered from the empirical research of Y community, and the scoring results showed that the total evaluation score was 4.275, which verified the applicability of the system and the effectiveness of the art of women's red art to create the atmosphere of “home” in the space. The validity of the system and the effectiveness of female red art in creating the atmosphere of “home” in the space is verified.
Copyright (c) 2026 Yi Yan, Deng Yue, Xie Shu Bin https://creativecommons.org/licenses/by/4.0/
Construction of Traditional Architectural Culture Inheritance and Innovative Design Talent Cultivation System Empowered by Digital Technology
As an important part of Chinese civilization, traditional architectural culture carries profound historical heritage and cultural connotation. In the context of accelerated urbanization, traditional architectural culture faces the double challenges of inheritance fault and innovation development. Digital technology provides new development opportunities for traditional architectural culture inheritance and innovative design talent training. This study constructed a model of influencing factors on talent cultivation quality containing four dimensions: student body, university management, social environment, and family factors, and used PLS-SEM structural equation modeling analysis to conduct an empirical study by distributing 340 questionnaires and recovering 317 valid questionnaires. The results show that the overall Cronbach's α value of the questionnaire is 0.9327, the KMO value is 0.8957, and the cumulative variance explained rate of the four factors is 62.59%; the correlation analysis shows that the correlation coefficients of the student body, university management, social environment and family factors and talent cultivation are 0.7839, 0.8047, 0.7944 and 0.8269, respectively, which have reached a 0.001 significant level; structural equation model analysis found that the total effect of the student body on the social environment is 0.4029, and the coefficient of direct influence of the social environment on talent cultivation is 0.9454. The study finally constructed the disciplinary construction goal of “optimizing structure, strengthening connotation, deepening education reform, and catalyzing results” and the practical teaching system of “four rounds” practical training driving method, which provides a theoretical basis for the cultivation of talents of traditional architectural culture inheritance and innovation design and provides a theoretical basis for the training of talents of traditional architectural culture inheritance and innovation design. It provides theoretical basis and practical guidance for the cultivation of traditional architectural culture inheritance and innovative design talents.
Copyright (c) 2026 Chunhui Yang, Liying Rong, Wenru Zhao https://creativecommons.org/licenses/by/4.0/
Blockchain-based data protection scheme and cybersecurity framework for housing finance trading platforms
Currently housing finance transaction platforms face challenges of data protection and cybersecurity. Blockchain technology, with its decentralization, non-tampering and high transparency, has become an effective tool for securing transaction data. In this paper, a blockchain-based data protection scheme for housing finance transaction platform is designed, which combines the shared energy storage system and realizes the cyber security protection of the transaction platform by optimizing the PBFT consensus mechanism. Methodologically, distributed file storage technology (IPFS) and smart contracts are adopted to ensure data encryption, storage and transaction transparency. Experimental results show that the proposed scheme excels in smart contract execution time, with a maximum execution time of 0.8ms, and achieves a significant increase in TPS when the concurrent volume of transactions reaches 1,200, and the throughput of the dual-chain architecture is increased by 28% compared to the traditional single-chain architecture. In addition, the system with ITPBFT consensus mechanism reduces the communication overhead by 46.19% compared to the traditional PBFT, and the consensus delay is also significantly reduced, with an efficiency improvement of 53.61%. The study shows that the proposed optimization scheme can enhance the efficiency and reliability of data transactions while improving the security of the system.
Copyright (c) 2026 Fang Yang https://creativecommons.org/licenses/by/4.0/
The multifunctional design of English home learning spaces promotes language interaction among family members
This paper investigates the multifunctional design of English home learning spaces based on the Kano-QFD model. Affinity diagramming is used to organize questionnaire data, and the Kano model is applied to calculate SII and DDI values. The refined SII and DDI data are then incorporated into the QFD quality house for weight analysis of user requirements. Finally, based on the Kano-QFD model, the weight values of user requirements for modular learning spaces are obtained to provide further guidance for the modular design of learning spaces. Based on the Kano-QFD model, this paper proposes renovating home learning spaces from three aspects: electronic resource spaces, leisure and social spaces, and activity spaces. To verify that the renovated learning space can effectively enhance family members' language interaction, this paper designed a control experiment to compare the language interaction effects of family members under the traditional home learning space. The study indicates that the English home learning space designed based on the Kano-QFD model can effectively enhance family members' language interaction.
Copyright (c) 2026 Ruijiao You https://creativecommons.org/licenses/by/4.0/
Research on the Innovation of College English Teaching Based on Blended Learning Model
In college teaching, college English still has some problems in classroom interaction, course content and teaching mode. In response to these challenges, blended learning has emerged as an emerging teaching mode. This paper integrates the blended learning mode in college English classroom for teaching innovation. It also takes the online classroom of a college English course in a freshman class of a university as the research object, and utilizes the UCINET social network analysis tool to study the network structure of interactive learning of college English under this mode. Teacher 1 had 1248 interactions in the course discussion forum, and 95% of the students had 100 or more interactions, with the lowest number of interactions reaching 80. It shows that the hybrid course has a high frequency of interaction. The eigenvector centrality value of Teacher 1 is 0.671, indicating that the teacher is the most central member of the online classroom. Students 14, 18, 3, 12, 20, 7, and 4 all have nEigenvec values of 20 or more, indicating that they are more central figures in the online classroom. The analysis of the factions of the sub-structures within the social network shows that Teacher 1 and Students 22, 25, and 28 appear several times in different subgroups, indicating that these four have more frequent interactions with other actors and are the core actors in the synchronous online classroom. The results of the study show that the innovation of college English teaching in blended mode proposed in this paper has strong feasibility and can help students better participate in the classroom and communicate with the teacher.
Copyright (c) 2026 Siyuan Yuan https://creativecommons.org/licenses/by/4.0/
Home as a Cultural Metaphor for Housing Space in Contemporary Literature
As a mirror image of social reality, literary works frequently use the image of “home” to carry complex cultural connotations of housing space. The concept of “home” in contemporary literature goes beyond the simple living space and becomes an important metaphor for carrying complex social and cultural meanings. In this study, we use a combination of text mining and sentiment analysis, bag-of-words model, TF-IDF algorithm and LDA theme model to systematically analyze “home” in contemporary literature. The keywords are extracted from the jieba thesaurus, the co-occurrence matrix is constructed, the clustering analysis is performed by SPSS, and the sentiment tendency is evaluated based on the Boson NLP Sentiment Dictionary. It is found that “home” in contemporary literature mainly presents four thematic dimensions: spatial alienation and material dilemma, identity loss and belonging anxiety, power structure and intergenerational conflict, emotional alienation and spiritual dilemma. The word frequency statistics show that Living dilemma ranks first with 1146 times, and Property maze ranks second with 973 times. The results of sentiment analysis showed that readers' negative attitudes toward such literature accounted for 73.76%, and positive emotions only accounted for 26.24%. The study reveals the deeper connotation of “home” as a cultural metaphor of housing space in contemporary literature, and provides a new perspective for understanding the cultural expression of housing problems in modern society.
Copyright (c) 2026 Jingwen Zhang https://creativecommons.org/licenses/by/4.0/
Research on the Innovation of English Teaching Mode in Colleges and Universities by Integrating Deep Learning Algorithms
In this paper, a new personalized recommendation system for English teaching resources is designed on the basis of deep learning, and a personalized English teaching model is innovatively proposed. The features of test information are continuously mined by convolutional neural network. Matrix decomposition technology and Bayesian criterion are adopted, so as to effectively characterize the association between users and English teaching resources, thus completing the recommendation of teaching resources. With the help of questionnaire data, the current status of the teaching practice of the Civics Smart Classroom in colleges and universities is understood. The test results show that the average test scores (P=0.001) and the degree of knowledge mastery (P=0.0045) of the experimental class and the control class are significantly different, indicating that the implementation of the personalized teaching mode based on the deep learning recommendation algorithm is effective. After the teaching practice, the students' independent learning, critical thinking, innovative thinking, problem solving, transfer thinking, effective communication, and cooperation ability are improved to different degrees.
Copyright (c) 2026 Lijun Zhang https://creativecommons.org/licenses/by/4.0/
Research on the training model of vocational education under the interface between technological progress and the housing industry
The new round of scientific and technological revolution and industrial change are profoundly reshaping the global economic landscape, and cutting-edge technologies such as artificial intelligence, big data, and Internet of Things are accelerating the penetration of traditional industries. The housing industry, as an important pillar of the national economy, is experiencing a critical period of transformation from traditional construction methods to intelligent, green and industrialized. In order to explore the optimization path of vocational education training mode under the interface of scientific and technological progress and housing industry, this study carries out a systematic analysis of the influencing factors of talent training based on the DEMATEL-ISM-MICMAC model and Bayesian network evaluation model. The study constructed an evaluation system containing a total of 15 influencing factors at three levels of multi-faceted policy, multi-dimensional environment and multi-dimensional innovation, and obtained data and conducted empirical analysis through expert survey method. The results show that: the centrality of participation in innovation enthusiasm, scientific research and innovation ability, and local government expenditure on vocational education rank in the top three respectively, of which the centrality of participation in innovation enthusiasm reaches 16.752; 55% of the probability of input-output performance of vocational education training mode is at a high level, and 58% of the probability of talent cultivation level is at a high level; the sensitivity analysis reveals that the sensitivity of researchers' full-time equivalence is the highest up to 15.45; Bayesian network analysis found that the probability of the growth rate of talent cultivation output can reach 0.585 when the growth level of school dormitory area is high. The study provides theoretical support and practical guidance for constructing a vocational education cultivation model adapted to the needs of scientific and technological progress and the development of the housing industry.
Copyright (c) 2026 Rui Zhang https://creativecommons.org/licenses/by/4.0/
With the help of big data algorithm to promote the practice of deep integration of traditional Chinese culture and ideological and political education
In today's educational environment, the application of big data technology has brought digital, precise and intelligent changes to ideological and political education. This paper adopts a questionnaire survey to count the level of integration of traditional Chinese culture and ideological and political education. On this basis, Word2Vec neural network and K-Means algorithm are fused, and word vector clustering method is utilized to mine students' developmental needs for the integration of Chinese traditional culture and ideological and political education, and to propose strategies for the practice of the deep integration of Chinese traditional culture and ideological and political education. Most teachers and students have a general knowledge of Chinese traditional culture, and over 70% of students have a strong willingness to integrate it into ideological and political education. The current integration teaching has problems such as single teaching method, lack of school management, and shallow integration. Students' needs in terms of teaching resources, teaching methods, teacher quality, hardware and software, and practical activities are also obtained, by which the integration practice strategies of enriching teaching resources, innovating teaching methods, optimizing teachers, building teaching platforms, and strengthening practical links are proposed to promote the deep integration of traditional Chinese culture and Civic and Political Education.
Copyright (c) 2026 Xiuli Zhang https://creativecommons.org/licenses/by/4.0/
Analysis of prediction and prevention countermeasures of shallow loess landslides in the construction of residential areas
In loess plateau areas affected by perennial heavy flooding, landslide disasters occur frequently, posing serious challenges to residential construction. In this paper, the prediction and prevention countermeasures of landslides are proposed by analyzing and predicting the monitoring data of loess landslides in a residential area under construction. The study used an intelligent variable frequency landslide displacement meter for displacement monitoring, combined with the moving average method and slope model for early warning of landslide occurrence. The results show that using this method, four sudden landslides were warned with high accuracy and the maximum advance warning time was 33 hours. The slope model performs well in the prediction of landslide destabilization time, and the confusion matrix analysis of the model shows that the prediction accuracy of 3m resolution data is the highest, with a true rate of 0.68, a false-positive rate of 0.40, and a precision of 0.65. The analysis of the success curve shows that the accuracy of the model prediction is improved significantly with the improvement of the resolution of data in the digital elevation model, which is especially suitable for the prediction of point landslides. In this paper, the reliability of the slope model is verified, and a method of landslide deformation stage division based on the tangent angle model is proposed, which provides a scientific basis for the prediction and prevention of landslide disasters in residential areas under construction.
Copyright (c) 2026 Yuming Zhang, Shiyang Lin https://creativecommons.org/licenses/by/4.0/
Construction of English Language and Literature Translation Quality Assessment Model Based on Deep Neural Networks
English literature translation is a systematic and complex project, the study is based on the assessment of English language and literature translation quality, mBERT, XLM-R, mBART are selected for cross-language pre-training, and a translation quality estimation model based on the pre-training model of Transformer encoder-decoder structure (mBART) is constructed. The mainstream pre-trained language models are selected for performance comparison, and the mBART-based translation quality estimation model proposed in this paper achieves the best results, surpassing the baseline model under different translation tasks in multiple datasets. The average absolute error and root mean square error of the model training in this paper are less than 0.15, and the correlation coefficients in English and German translations and English and Chinese translations are higher than those of the baseline model by 0~4% and 0~13.3%, and are less affected by the number of translated sentence types and utterances, which indicates the accuracy and stability of the translation quality estimation model of English language and literature in this paper. In conclusion, the proposed model is suitable for use in the assessment of English language and literature translation quality.
Copyright (c) 2026 Yu Zhao, Yunxia Yan https://creativecommons.org/licenses/by/4.0/
Research on Co-Creation Mechanisms and Innovation Strategies of ESG and Real Estate Enterprises Empowered by New Quality Productivity
As a resource-intensive industry, real estate firms have become the focus of research on how to improve ESG performance and cope with environmental and social responsibility pressures. The rise of new-quality productivity provides enterprises with the impetus to innovate, especially driven by technological and digital transformation, which enables real estate enterprises to better achieve green and sustainable development while optimizing resource allocation and enhancing productivity. This study examines the impact of new quality productivity on the ESG performance of real estate companies, using data from 1,200 real estate companies in Shanghai and Shenzhen A-shares over the period 2015-2024 for empirical analysis. It is found that there is a significant positive correlation between new quality productivity and firms' ESG performance. Specifically, the improvement of new quality productivity can significantly promote firms' ESG performance, especially the effect is more significant in firms with higher technology level. In addition, financing constraints play a partial mediating role in this process. The empirical results show that real estate firms can further enhance their market competitiveness by optimizing resource allocation, reducing environmental impacts, enhancing social responsibility and optimizing governance structure while improving the level of new quality productivity. Data analysis shows that the positive impact of new quality productivity on corporate ESG is particularly prominent among high-growth enterprises, and the digital transformation and technological innovation of enterprises have become key factors driving this process. The study suggests that enhancing new quality productivity not only helps to promote the high-quality development of enterprises, but also provides an important guarantee for the sustainable development of the real estate industry.
Copyright (c) 2026 Fuping Zhou https://creativecommons.org/licenses/by/4.0/
A Study of Digital Marketing in the Housing Market and the Prediction of Consumer Purchasing Behavior
Driven by the wave of digital economy, the real estate industry is facing the transformation of marketing methods. Traditional marketing means have been difficult to meet the precise and diversified consumer demand, and digital marketing means have gradually become an important strategy in the housing market due to its advantages of high efficiency and wide coverage, which has pushed the research on consumer behavior prediction to become an emerging hot spot. This study focuses on the application of digital marketing in the housing market and explores how to predict consumer purchase behavior through multi-model fusion. First, the behavioral data of 13 million users of the real estate e-commerce platform are preprocessed, and the SMOTE and Borderline-SMOTE methods are used to achieve data balance. In terms of modeling, three single models, logistic regression, random forest and XGBoost, are selected for prediction, and the prediction performance is enhanced by Voting fusion algorithm. The empirical analysis results show that the F1 value of Random Forest among the single models is the highest up to 73.68%, while the weighted voting fusion model performs the best in terms of comprehensive performance, with the F1 value elevated to 81.51%, and the AUC value up to 0.6723. The results verify the effectiveness of the fusion model in predicting the consumer purchasing behaviors, and provide data support and technical basis for the implementation of precision marketing in the housing market. The conclusion states that digital marketing tools combined with multi-model fusion prediction technology can significantly improve the conversion rate of home purchase and user identification.
Copyright (c) 2026 Lanlan Zhou https://creativecommons.org/licenses/by/4.0/
Logistic Regression Analysis of Operational Risk and Financial Management in Cloud Accounting Model in Big Data Environment
In the production activities, from the financial risk can detect the risk of the enterprise's various business activities, the management of financial risk will also be for the enterprise's business management, capital utilization to improve the proposal. This paper constructs a logistic regression model, uses the great likelihood estimation method to solve the model, and derives the formula for calculating the parameter estimates according to the Logistic function. The sample of the tested enterprise is established, and the model of financial early warning index system is constructed by combining the reasons for the formation of enterprise financial risk and related research. The model is applied to the estimation of the financial risk level of enterprises after passing the test. During the period of 2020-2024, the financial risk warning value of the subject A enterprise is 73.6563, 55.877, 63.3313, 74.9454, 84.545, and the risk level is level 8 risk, level 6 risk, level 7 risk, level 8 risk, and level 9 risk, respectively, and the financial risk of this enterprise is increasing year by year. Analyzing the specific financial indicators of this enterprise, the cash ratio declined slightly from 0.9264 in 2020 to 0.8496 in 2021, and has been fluctuating during 2021-2024, with a value lower than 1, which indicates that the actual quality of profitability of this enterprise is not good, and it needs to draw the attention of the management. For the financial management problems of enterprise A, relevant countermeasures are proposed.
Copyright (c) 2026 Hongyan Zhu https://creativecommons.org/licenses/by/4.0/
Research on College Students' Physical Education Teaching and Training Load Control Methods Empowered by Intelligent Algorithms
With the improvement of people's living standards, sports and health have become the focus of attention, and sports monitoring systems are increasingly favored. In this paper, we design the training load control method for college students' sports teaching, propose the linear fitting sports data feature fusion method and the weighted matrix decomposition recommendation algorithm WSVDLFM based on the implicit semantic model to realize the intelligent monitoring of sports and personalized sports guidance recommendation. The experimental results show that the sports monitoring system based on the proposed data feature fusion method can effectively realize the instant sampling and fusion optimization of sports training data, and the data collection accuracy is always maintained above 92%. Meanwhile, the WSVDLFM algorithm is able to realize the accurate recommendation of sports guidance, and outperforms other comparative algorithms in both HR and NDCG. In addition, the proposed sports training method “Fun Interval Running” can maximize the efficiency of students' sports training under the premise of controlling students' training load through real-time monitoring of heart rate and pace, which is of great significance for college students' sports teaching.
Copyright (c) 2026 Rongchao Zou https://creativecommons.org/licenses/by/4.0/
A Path to Enhance the Effectiveness of Secondary School English Vocabulary Teaching Empowered by Natural Language Processing in the Era of Artificial Intelligence
With the rapid development of science and technology, artificial intelligence (AI) technology has been deeply integrated into the field of education, and the teaching of English at the secondary school level, as a key link in the cultivation of high-quality skilled talents, is in urgent need of innovative change. This paper firstly proposes a secondary school English vocabulary teaching model using natural language processing (NIP) technology, which integrates key technologies such as Wikipedia corpus, semantic similarity computation, Word2vec tool, etc., aiming at solving the dilemmas faced in the traditional English vocabulary teaching process. Finally, the teaching practice shows that the experimental group is significantly higher than the control group in learning motivation (affective experience, cognitive level and behavioral tendency) in relation to test scores (p<0.05). Therefore, the use of NIP technology has brought a positive impact on the reform of English vocabulary teaching in secondary schools.
Copyright (c) 2026 Shixuan Chang https://creativecommons.org/licenses/by/4.0
Exploring the Process Re-engineering and Effectiveness Enhancement Path of Academic Affairs Management in Colleges and Universities Empowered by Digital Transformation
Academic affairs management is the core of all management work in colleges and universities, and all colleges and universities are actively promoting the scientific and standardized management mode, and the management means are also moving towards digitalization and automation. In this paper, an efficient and accurate transformation prediction method for the digital transformation of academic affairs management is constructed based on the XGBoost algorithm, and the results of the students' academic early warning are divided into three categories. At the same time, feature selection as well as uneven data processing are performed on the data. The accuracy rate and F1 score were used as evaluation indexes to compare the XGBoost prediction model with SVM, RF and other algorithmic models, and SHAP was used to analyze the interpretability of the model. Data analysis showed that XGBoost predictive performance was good (F1 value of 0.879) and better than SVM, RF and other models. The influence of data accuracy, first semester grades, and educational support on the energy efficiency of digital transformation of academic affairs management is more obvious, and there is a significant difference in the influencing factors for the digital transformation of academic affairs management at different levels of transformation effectiveness.
Copyright (c) 2026 Yuqiu Wang , Aida Hanim Binti A. Hamid https://creativecommons.org/licenses/by/4.0
Changes in demand and optimization of cultivation paths of smart financial talents for corporate carbon reduction and pollution reduction under the risk constraints of artificial intelligence application: a mixed research approach
Environmental pollution and climate change are major challenges on the road to sustainable development for human society. In this regard, based on the risk constraints of the application of artificial intelligence, this paper takes 2712 A-share listed manufacturing enterprises from 2012 to 2024 as research objects, and utilizes the mixed research method of qualitative combined with quantitative analysis to examine the impact of artificial intelligence on the reduction of pollution and carbon dioxide of enterprises, as well as the impact of carbon dioxide reduction and pollution reduction of enterprises on the demand for intelligent financial talents. The study shows that the application of artificial intelligence can effectively reduce the sulfur dioxide and carbon dioxide emissions of enterprises, and play the effect of pollution reduction and carbon reduction. The regression coefficients of the degree of demand for intelligent financial talents in enterprise pollution reduction and carbon reduction are 0.273 and 0.234 respectively, indicating that for every unit increase in pollution reduction and carbon reduction, the demand for intelligent financial talents in enterprises will increase by 0.273 and 0.234 units respectively. At present, the market demand for intelligent financial talents has formed a large gap, the traditional financial education training path, and can not meet the current economic and social demand for talents, there is an urgent need to explore the new path of high-quality training of intelligent financial talents.
Copyright (c) 2026 Lei Chen, Fengjuan Yu, Shuai Zeng https://creativecommons.org/licenses/by/4.0
The Impact and Integration Path of Artificial Intelligence Composition Technology on the Traditional Music Composition Mode
With the rapid development of deep learning technology, artificial intelligence composition technology has been able to generate musical works that are basically equal to traditional music composition in terms of sensory indicators, which has made a substantial impact on the main position of traditional music composition mode. In this paper, recurrent neural networks are used to learn the time-dependent features of musical sequences, and then generate adversarial networks to enhance the authenticity of the musical score, and express the music potential space in the form of probability distribution parameters. Rule-based algorithms are introduced to provide intervenable structural constraints, effectively generating scores with original musical style and rhythmic characteristics, and based on the above methodology, we explore the impact and integration possibilities of AI on the traditional mode of music creation. Experiments show that the PC and PI values of the AI composition method based on the RVAE-GAN model are improved by 8.57% and 5.89% compared with the Music Transformer model. The AI composition technique approaches the average level of traditional human work composition mode in terms of sensory acceptance and specific style maintenance. The above results confirm that the impact of AI on the traditional music composition mode is substantial, and the integration paths such as emotion-structure dual-drive and complementary style inheritance are feasible directions to cope with the impact.
Copyright (c) 2026 Zimu Mao , Mengxin Mao https://creativecommons.org/licenses/by/4.0
Unpacking the Mechanism: How Professional Learning Communities Influence Primary School Teachers' Self-Efficacy
This paper investigates the mediation mechanisms through which professional learning communities shape primary teachers' self-efficacy in various educational systems. Based on the data from 15,847 teachers in 16 systems, a multi-level mediation analysis was conducted to test the mediation effects of teacher collaboration, professional development, and job satisfaction. Professional learning communities were measured by aggregating collaborative activity at the school level, while self-efficacy was assessed by the TALIS Teacher Self-Efficacy Scale. The study employed hierarchical linear modeling and multi-level structural equation modeling, which involved the use of bootstrap methods, to address the nested data structure. The findings show that professional learning communities had a positive total standardised effect of 0.43 on self-efficacy, where teacher collaboration was the strongest mediator (indirect effect = 45.8%), followed by job satisfaction (indirect effect = 37.5%) and professional development (indirect effect = 16.7%). The direct effect was significant at 0.19, indicating the presence of residual mechanisms. Cross-country analyses disclosed wide variations, where teacher collaboration was most pronounced in Nordic systems (0.14-0.17), while job satisfaction was most salient in East Asia (Japan = 0.16, South Korea = 0.15). The study contributes to theory by shedding more light on the mechanisms for efficacy development, while its findings can be utilised to develop culturally adapted professional learning activities to boost primary teachers' confidence by engaging them in genuine collaborative activities.
Copyright (c) 2026 Haoyuan Wang, Zuraidah Abdullah, Adibi Rahiman Md Nor https://creativecommons.org/licenses/by/4.0
A Systematic Analysis of the Construction Strategies and Implementation Effects of Students' Oral Technical Communication Competence in the Field of TVET in the M-learning Environment
With the rapid development of Internet technology, emerging concepts such as mobile learning (M-learning), cloud computing, and technical and vocational education and training (TVET) have gained increasing attention in the field of education. Based on the existing research results, this study adopts WeChat as a platform for students' oral technical communication and proposes a strategy for building their oral technical communication competence from three different dimensions. Subsequently, the target of strategy implementation is selected, and a detailed implementation plan is also formulated. In order to systematically and intuitively demonstrate the implementation effect of oral communication strategy in TVET domain under the M-learning environment, the Analytical Hierarchy Process (AHP), Entropy Weighting Method (EWM), and cloud model-based comprehensive evaluation are employed to carry out in-depth exploration and analysis. The results yield a comprehensive evaluation cloud of (79.27, 3.29, 1.82), which is closest to the “good” level corresponding to the standard cloud (80.5, 2.5, 0.5). This indicates that the implementation effect of the proposed oral communication strategy is good, demonstrating its practical effectiveness in the TVET context under an M-learning environment.
Copyright (c) 2026 Jie Liang, Marlissa Omar, Intan Farahana Kamsin https://creativecommons.org/licenses/by/4.0
Application and Effect Evaluation of Blended Teaching Model of Higher Vocational Music Education in the Context of AI Era
In the music education industry, the use of artificial intelligence technology has greatly enriched education and teaching, education and training, music APP, intelligent systems and other aspects, injecting fresh blood into teaching activities. Based on this background, the study proposes a hybrid teaching model for English education by combining online and offline learning modes. Aiming at the deficiencies of collaborative filtering algorithm and neurocognitive diagnosis, a PMF-C&RM learning resource recommendation method integrating cognitive diagnosis and collaborative filtering is proposed, and relevant experiments are set up for verification. Taking 80 people in the English major 2024 music education as research subjects, divided into experimental and control classes, the teaching practice method was used to teach the course in four stages. The research results show that the PMF-C&RM method achieves the optimal value compared with other models, with the highest F1 value of 0.928 for simple questions and 0.932 for difficult questions, which effectively improves the effect of learning resources recommendation. Analyzing the teaching effectiveness of hybrid teaching based on PMF-C&RM in music courses, the teaching model proposed in this paper is significant in improving students' comprehensive performance ability in music.
Copyright (c) 2026 Shifang Yang https://creativecommons.org/licenses/by/4.0
A Study of the Mechanisms of Generative AI Mobile Learning App's Impact on Vocational Students' Self-Directed Oral Technical Communication Skills
The rapid development of artificial intelligence (AI) has broken the time and space limitations of traditional classroom learning and provides vocational school students with a convenient and new forms of mobile learning supported by generative AI applications. In order to explore the impact of mobile learning app tools on students' oral technical communication skills, this study adopts a qualitative investigation and research design to analyze the current status of vocational school students' self-directed oral technical communication skills with a questionnaire, builds a controlled experimental process of generative AI mobile learning app-assisted intervention teaching, and synthesizes the survey results, quiz data, and interview transcripts to discuss the students' oral technical communication skills' changes. Considering the mechanism of further quantifying the improvement of the competence, research variables were set up, and logistic regression model was selected as the test tool to test and examine the specific connection between the two. The results indicate that both the experimental and control groups showed imporovements after the intervention; however, the gains in the control group were almost negligible. The independent samples t-test results also revealed that the post-test scores of the experimental group were significantly higher than those of the group (p<0.01). Logistic regression analysis further demonstrated that AI simulation realism, AI feedback adaptivity, app course fit, and ease of use of the app had significant positive effects on the learning outcomes. These findings confirm the positive role of generative AI mobile earning in promoting the development of vocational students' oral technical communication skills.
Teachers’ Roles and Teaching Behaviors in Mathematical Modeling Pedagogy
Mathematical modeling instruction, as a crucial approach to fostering students' mathematical application awareness and model-based thinking, necessitates a fundamental shift in teachers' teaching philosophies and behaviors. Adopting a "guidance-to-reconstruction" framework, this study examines the developmental path of teacher role transformation in this setting. The study first identifies the core objectives and behavioral characteristics of mathematical modeling instruction, arguing that the teacher's role evolves from a mere knowledge transmitter to a learning facilitator, context designer, and thinking facilitators. Through analysis of practical teaching cases and relevant literature, the paper reveals three stages of teacher role transformation: instructional guidance, modeling support, and instructional reconstruction. This progression demonstrates the teacher's crucial role in organizing problem situations, guiding students' modeling thinking, and promoting teamwork and reflection. The findings suggest that this behavioral shift constitutes not merely a methodological update but a fundamental reconceptualization of teachers’ professional identity. Finally, the paper proposes targeted recommendations for teacher development based on practical reflection and interdisciplinary integration, offering theoretical and practical insights for the advancement of mathematical modeling instruction.
Copyright (c) 2026 Yimiao Zhang, Nurul Nadwa Zulkifli , Ahmad Fauzi Mohd Ayub https://creativecommons.org/licenses/by/4.0
Project-Based Learning in the Era of Generative Artificial Intelligence: Toward a Hybrid Epistemic Governance Framework for Undergraduate Computer Science Education
The integration of generative artificial intelligence technology (GenAI) within higher education contexts is changing the epistemology associated with educational settings. In undergraduate computer science programs, project-based learning (PBL) has traditionally been considered one of the core pedagogical approaches to teaching based on constructivist epistemologies that emphasize purposeful knowledge construction and artifact production. The introduction of AI systems that can generate programming codes, designs, documentation, and feedback undermines the traditional association of cognitive work with epistemological development. This study presents an integrative theoretical approach referred to as Hybrid Epistemic Governance (HEG) in which PBL is rethought in relation to AI learning environments. Based on the principles of sociocultural theory, activity theory, distributed cognition, sociomaterialism, cognitive load theory, and the existing literature on AI literacy, this paper argues that the epistemological axis of pedagogy in the age of AI must shift from being production-centric to governance-centric. Through the HEG framework, this research explores four interrelated dimensions—epistemic discernment, algorithmic reflexivity, governance agency, and authenticity reconstruction—which collectively examine the ways in which learners engage with AI technologies through project work. This study analyzes the structural conflicts that typify the project-oriented nature of work environments facilitated by AI and articulates a set of principles for governance pedagogies along with an approach to future validation through a methodology. By framing project-oriented education as governance of hybrid human-AI knowledge systems, this research contributes to theory development in relation to generative AI technologies in higher education and offers a pragmatic framework for undergraduate computer science education.
Copyright (c) 2026 Tao Zhang, Gede Rasben Dantes, Dessy Seri Wahyuni, Made Hery Santosa https://creativecommons.org/licenses/by/4.0
A Data-Driven Framework for Metaverse-Based Chinese Learning System Design Based on Knowledge Flow and Open Innovation
The emergence and fast growth of metaverse technologies have brought new possibilities to the field of immersive and interactive learning system architectures. Nonetheless, the current research is mainly concerned with the technological affordances and learning outcomes, and less emphasis is placed on the systematic organization of learning spaces in the metaverse. A solution to the gap is provided by this study which develops a data-driven framework of metaverse learning space design depending on knowledge flow and open innovation. It is a multi-stage methodology that combines practitioner knowledge, expert validation, and large scale data generated by users. In the first place, more than 11,000 records were gathered through Python-based web scraping of online platforms to find out patterns of learning activities. Secondly, five Chinese language teachers took part in structured discussions to interpret and refine these patterns into pedagogically meaningful categories. Lastly, expert validation was performed to confirm conceptual consistency. The knowledge management lifecycle, which was expanded to cover knowledge storage, was taken as an analytical framework. The findings indicate that knowledge acquisition (0.345) and practice (0.244) are prevalent in modern learning system architectures, whereas knowledge storage (0.170) is an important factor, and knowledge creation (0.060) is not well represented. These results led to a functional architecture of metaverse learning system architectures being created, which included the lecture, activity, materials storage, and assessment functions. Both theory and practice are enriched by this study because it (1) expands the knowledge management lifecycle, (2) introduces a quantitative modeling approach to the concept of knowledge flow, and (3) provides a systematic way of converting knowledge processes into learning space design. The suggested framework will provide a scalable solution to creating adaptive and knowledge-based metaverse learning system architectures.
Copyright (c) 2026 Xin Liu, Pitipong Yodmongkol https://creativecommons.org/licenses/by/4.0
Judicial review and remedies for algorithmic bias in the application of environmental law
In this paper, the definitions of bias and fairness in deep learning are systematically sorted out, and the specificity of algorithmic bias in environmental law application scenarios is clarified. An algorithmic fairness testing method EIDIG based on gradient search is proposed for uncovering and correcting algorithmic bias.EIDIG adopts the gradient of the model output to replace the gradient of the loss function, which reduces the computational load of the model. Combined with the clustering algorithm to generate diverse individual bias samples as inputs for the next stage, the global search is completed. In the local search, the generated individual bias samples are imported and repeated detection is performed around them. The experimental results show that compared with the comparative fairness testing methods such as ADF and AEQUITAS, the number of bias samples generated by EIDIG, the success rate, and the generation efficiency are all improved to different degrees, and the time taken by EIDIG to generate 1,000 bias samples is only 43.90% and 88.06% of that taken by AEQUITAS and ADF, and EIDIG has achieved a leading position in improving the original model's fairness by achieving leading performance. Finally, this paper proposes judicial remedy strategies such as adjusting the civil law tort liability framework and introducing the public interest litigation system. It provides theoretical support for realizing the rule of law guarantee for environmental disputes in the era of artificial intelligence.
Copyright (c) 2026 Bona Song https://creativecommons.org/licenses/by/4.0
Study on the Mechanism for Guaranteeing the Right to Automated Processing of Personal Information in Environmental Governance
Aiming at the privacy risk brought by the automated processing of personal information in environmental governance, this paper proposes a set of systematic rights protection mechanisms. Firstly, the kernel principal component analysis method is used to reduce the dimensionality of information attributes by reducing the dimensionality and noise reduction of personal information. Then based on the privacy protection technique of stream cipher, the peak density clustering (CFSFDP) algorithm is introduced to improve the k -TBM anonymization algorithm, and a privacy protection method based on the k-TBM anonymization model of CFSFDP (CFSFDP-k-TBM) is designed. Finally, this paper conducts experimental tests on the CFSFDP-k-TBM algorithm, and compares and analyzes it. The test results show that CFSFDP-k-TBM algorithm has more effective privacy protection effect while ensuring users' personalized privacy needs. Therefore CFSFDP-k-TBM based anonymization model is an applicable privacy protection method for personal information in environmental governance.
The Impact of Social Media Interaction Frequency on Purchase Intention: Evidence from the Guiyang IT Market Mediated by Consumer Trust
With the development of social media platforms, the importance of social media marketing is gradually realized by major brands. This paper constructs an SEM test model containing a measurement model and a structural model, discusses the influence of social media participation on consumers' purchase intention, proposes the mediating role of brand equity and the moderating role of AI participation, and puts forward the corresponding research hypotheses for the above influencing roles. Questionnaires are designed to screen target users of social media platforms, research data are collected, data reliability and validity are examined, and relationships between variables are measured through SEM paths. The AVE values of the indicators of social media participation, such as liking and commenting, content creation, attention and interaction, and emotional investment, are all greater than 0.75, which has the fit validity. The standardized path coefficients of different dimensional variables on brand equity are 0.192, 0.162, 0.159, and 0.206, respectively, and social media engagement significantly and positively influences consumer purchase intention through the mediating bridge pair of brand equity. The findings are instructive for brand equity to enhance the marketing effectiveness of social media platforms.
Copyright (c) 2026 Yidan Xu, Arti Pandey https://creativecommons.org/licenses/by/4.0
A model construction of personalized teaching of English writing based on reinforcement learning
By constructing a deep knowledge tracking model based on reinforcement learning theory, students can choose more suitable courses or exercises when selecting learning resources. In view of this, the article proposes a temporal convolutional knowledge tracking model (TCKT-FI) based on reinforcement learning, which obtains students' knowledge mastery state through temporal convolutional network, solves the long sequence dependency problem by using causal convolution and inflationary convolution, and solves the gradient vanishing and exploding problems by using residual network to deal with the deep network structure. Following that, a recommendation algorithm framework based on Deep Reinforcement Learning (DRR) is proposed, and better recommendation strategies (Actor) and value functions (Critic) are explored. Finally, the model's English writing knowledge tracking effect is tested and its performance is verified through experiments. The knowledge tracking effect test experiment shows that at moments 1.1~5, 5.8-9.5, 10.5-15, when students did not learn the knowledge point, the TCKT-FI model shows a significant downward trend, indicating that students have forgotten the knowledge point, but the comparison model fails to simulate the forgetting process of students well. This confirms the accuracy of the model and the efficiency of its calculation. It has practical value in the personalized teaching of English writing.
Copyright (c) 2026 Shali Jin, SANITAH BTE MOHD, Norliza Mohamad, Abdulmumini Inda https://creativecommons.org/licenses/by/4.0
A Practical Path of Civics Case Bank for “Understanding Contemporary China” University English Course from the Perspective of Blended Learning Model
In recent years, the organic integration of curriculum Civics into the cultivation of English majors has become a hot issue in the education sector. On the basis of the known Civic-Political value of the Understanding Contemporary China series of textbooks, we obtain the Civic-Political cases of Understanding Contemporary China for university English courses through data mining and preprocessing, and use BERT, BiLSTM, and CRF to categorize the Civic-Political cases, and ultimately construct a kind of Civic-Political case base for university English courses that integrates Understanding Contemporary China. Subsequently, a combination of the case base and the blended learning model was used to establish a practice path for Civics and Politics in college English courses from three aspects: teaching evaluation system, classroom teaching design, and case design, and to analyze it with empirical inquiry. There is a certain difference and a big gap between the average scores of the two classes, and the difference between the average scores of their scores is 12.1, and there is a significant difference at the level of 0.05, which indicates that the use of the blended learning model to incorporate the Civic-Political cases in the university English course of Understanding Contemporary English has an obvious role effect. In addition, based on the results of the questionnaire, it can be seen that the experimental class students' knowledge mastery, thoughts and emotions have been significantly improved, which fully verifies the feasibility of the research program in this paper.
Copyright (c) 2026 Xuejiao Wei https://creativecommons.org/licenses/by/4.0
A Study on the Impact of Algorithmic Bias on the Fairness of Film and Television Communication and Regulation
Algorithmic bias is the bias of the algorithm in the operation process, resulting in unfair and unreasonable repeatable results, this paper starts from the perspective of algorithmic recommendation to explore the impact of algorithmic bias on the fairness of film and television dissemination. Using video feature data and user behavior data of short video film and television area as research objects, identifying the fairness of film and television dissemination, selecting feature variables of algorithmic bias, constructing a multiple linear regression model of the influence of algorithmic bias on the fairness of film and television dissemination, and introducing SHAP interpretation framework to quantify and attribute the importance of each feature variable. The results found that recommendation diversity, equalization of opportunities, and population parity have significant positive impacts on the fairness of film and television communication, and the exposure inequality index and discovery path bias both have significant negative impacts on the fairness of film and television communication, which are all significant at the 5% level, and their impacts on the fairness of film and television communication are different in terms of the way, direction, and strength of their impacts. Combined with the advanced regulation experience of today's society and various social media platforms, suggestions and countermeasures are proposed for the regulation direction of algorithmic recommendation.
Copyright (c) 2026 Zhou Hong, Xiaoting Xu https://creativecommons.org/licenses/by/4.0
Analyzing the Effect of Task-Based Pedagogy on Learning Effectiveness in English Classroom Based on Computational Linguistic Modeling
This paper proposes to use computational linguistic modeling to assess teaching effectiveness, with the help of technical methods such as Wasserstein distance and cosine similarity to assist in the diagnosis of teaching effectiveness. Taking the English teaching of two ordinary classes in a key high school as the research object, the impact of task-based teaching on English learning effect is analyzed. The questionnaire method was utilized to investigate the changes in students' evaluation of English learning before and after the experiment. The results show that the mean value of the total grade of the experimental class using task-based teaching is 119.4973, which is about 35 points higher than the mean value of the total grade of the control class, and the test of difference in grades shows that sig=0.00<0.05, the difference in the grades of the two classes is significant, which verifies that the task-based teaching method can effectively improve the English learning effect of the students.
Copyright (c) 2026 Hongxia Zhang https://creativecommons.org/licenses/by/4.0
Application of Improved BP Neural Network in Predicting Teaching Effectiveness of Information Literacy Education in Colleges and Universities
This paper takes the evaluation of teaching effect of information literacy education in colleges and universities as the research object, and establishes the evaluation index system of teaching effect of information literacy education, which includes 13 first-level indexes and 30 second-level indexes in the four dimensions of students' information consciousness, information application, information ethics, and information ability. The BP neural network improved by particle swarm optimization algorithm is used to establish a mathematical model for evaluating the teaching effect of information literacy, which uses the BP model trained by PSO to fit the complex relationship between the many indicators affecting the evaluation of the teaching effect of information literacy in colleges and universities and the evaluation results. Through the data research on undergraduates of University D and training analysis using the research data, it is concluded that the training error of PSO-BP algorithm is stabilized at less than 0.02 after 5000 steps of training, and the training time of the model is 10 s. The BP neural network improved by the PSO algorithm is more stable than the pre-improvement connection weight learning process and achieves the ideal error accuracy faster, and its predicted output value is basically in line with the expected value. Its predicted output value is basically consistent with the expected value. The evaluation results also show that the information literacy education in School D needs to be strengthened in terms of information awareness and information application. It shows that the PSO-BP model can be used to effectively predict the effectiveness of information literacy teaching in colleges and universities.
Copyright (c) 2026 Xin Wang, An Liao, Jinyuan Zhang, Jingqiu Zhang, Yucen Shi https://creativecommons.org/licenses/by/4.0
Collaborative Governance of Cross-border Transportation Security in Central Asia under the Perspective of Digital Empowerment - A Simulation Analysis Based on Evolutionary Game Models
In recent years, the trade volume between China and the five Central Asian countries has maintained a high growth rate, which has led to the growth of the demand for international trade transportation services between China and the five Central Asian countries, and the ensuing cross-border transportation risks have gradually come into people's view. This paper first analyzes the current situation of cross-border transportation between China and the five Central Asian countries, including trade scale, logistics performance, logistics infrastructure and security risks. Secondly, based on the evolutionary game model, it analyzes the game of the three parties (governments, cross-border transport enterprises, and consumers) in the process of collaborative governance of cross-border transport safety between China and the five Central Asian countries. Then, Matlab is used to conduct numerical simulation analysis to explore the influence of collaborative governance factors in more depth, and optimization strategy analysis of different combinations of factors is conducted to study the importance of collaborative governance factors in depth. The simulation results show that the government is in the leading position in the process, cross-border logistics enterprises will choose the “cost priority” strategy in their own interests, and consumers, as the ultimate beneficiaries of cross-border transportation services, are highly motivated to participate in the collaborative governance of cross-border transportation safety. Finally, based on the current situation analysis and game simulation results, an optimization path of China-Central Asia cross-border transportation security collaborative governance is proposed based on the SFIC model, in order to better exert the synergistic effect of the three parties.
Copyright (c) 2026 Chenran Song, Yulong Liang https://creativecommons.org/licenses/by/4.0
Combining Textual Emotion Analysis and Gender Role Theory to Study the Portrayal of Women in Seven Male Maodun Literature Prize Winning Works
In this paper, we construct a text sentiment analysis model based on BERT-Attention-BiLSTM, which is combined with the gender role theory to analyze the female character images in the award-winning works of male Mao Dun Literature Prize.The BERT-Attention-BiLSTM model combines the BERT model as a word vector training model, which can solve the word in word vectorization with the The BERT-Attention-BiLSTM model combines the advantages of the BERT model as a word vector training model in word vectorization, which can solve the problem of multiple meanings in word quantization, with the introduction of the Attention mechanism which distinguishes the degree of influence of different words in the text on the task of sentiment classification. By comparing with several text sentiment analysis models, the Bert-BiLSTM-Attention model has an accuracy of 81.20%, and the value of F1 is 58.42%, which is the best among all models. Then, the basic information and interpersonal network of seven Mao Dun Literature Prize works were quantitatively studied, and four works were selected for quantitative analysis of emotional trends, and the emotional trend lines portrayed fit with the plot development of the works. This study provides a literary perspective reference for understanding the interaction between female development and social progress.
Copyright (c) 2026 Bihan Wang, Burin Srisomthawin, Xiaolong Zheng https://creativecommons.org/licenses/by/4.0
Identifying Patterns of Learners' Cognitive Styles on Differences in English L2 Lexical Expression through Cluster Analysis
This study aims to investigate the pattern of association between English learners' cognitive styles and their second language vocabulary expression differences. The study used the Embedded Figures Test (EFT) to measure the cognitive styles of the subjects, categorized the subjects into two cognitive styles: field-independent and field-dependent, and used the acquisition test to explore the timely and delayed effects of the subjects' field cognitive styles on the acquisition of second language vocabulary in English. The LSD method was used to verify whether the learners' different cognitive styles made a difference in English bilingual vocabulary expression, and then the k-means clustering analysis was used to derive the pattern of difference between the two. The results show that learners can be classified into three typical patterns: "field-independent - high retention", "field-dependent - high immediacy", and "field-dependent - low efficiency". Different cognitive styles of learners result in different types of second language vocabulary expression differences. The findings of this paper provide empirical evidence and data support for the subsequent implementation of individualized vocabulary instruction and assessment.
Copyright (c) 2026 Junling Wang, Suthagar Narasuman https://creativecommons.org/licenses/by/4.0
Improved nicr-nisi thin-film thermocouple manufacturing process enables accurate temperature measurement of UAV engines
UAV engine turbine blade temperature measurement is an important basis for its working condition monitoring and health management, and high-temperature thin film thermocouple is considered to be an effective method to solve the problem of its temperature measurement in extreme environment. In this paper, the K-type NiCr-NiSi thin film thermocouple is taken as the research object, and the NiCr-NiSi thin film thermocouple is prepared on Ni-based superalloy substrate using electron beam evaporation and magnetron sputtering, and the improved thin film thermocouple consists of the Ni-based superalloy substrate, the NiCrAlY transition layer, the Al2O3 thermal oxide layer, alumina insulating layer, NiCr-NiSi thin-film thermocouple layer, and alumina protective layer. The results of calibration and compositional tests of the thin film thermocouple samples show that the improved NiCr-NiSi thin film thermocouple has a good linearity of Seebeck coefficient of 40.5 μV/K in 50~650 ℃, which is slightly lower than that of the standard thermocouple of type K with Seebeck coefficient of 41.0 μV/K. The dynamic performance of the temperature sensors has been investigated and tested both theoretically and experimentally, and the theoretical and experimental performance of the thermocouple has been investigated and tested. The dynamic performance of the temperature sensor was investigated and tested from both theoretical and experimental points of view. The theoretically calculated time constant was 38.5 μs, and the experimentally tested time constant was 85.05 us, with the response time in the same order of magnitude. The temperature measurement performance of the temperature sensor tested in a constant temperature field can meet the temperature testing requirements in the environment of up to 650°C, and can be reliably applied to the accurate temperature measurement of UAV engines.
Copyright (c) 2026 Jiayi Sun https://creativecommons.org/licenses/by/4.0
Research on Intelligent Planning of the Path to Improve Intercultural Communication Competence of Business English in Colleges and Universities Driven by Big Data
Aiming at the limitations of the traditional teaching mode and the failure of big data technology to realize the deep integration with the whole process of teaching, the study proposes a cross-cultural communication integration teaching mode based on personalized teaching resources recommendation algorithm. The personalized teaching resource recommendation model proposed in this paper is a personalized teaching resource recommendation method based on cognitive diagnosis and collaborative filtering, MNCD-NMF, which combines multi-factor neurocognitive diagnosis and neural matrix decomposition model to model students' knowledge mastery and obtain students' personalized learning characteristics. Then, the personalized learning characteristics are integrated into the neural collaborative filtering model to predict the students' responses. Finally, a list of recommended learning resources was provided to students by setting different difficulty parameter ranges. Experimental and control classes were designed for teaching practice. The mean values of the four dimensions of intercultural communication competence in the experimental class were 3.45, 3.37, 3.19, 3.23 for the knowledge, awareness and behavior dimensions respectively, and 2.25, 2.29, 2.20, 2.27 for the control class, respectively, and the cross-cultural communication integration teaching model can promote the intercultural communication competence in the knowledge, awareness, affective and behavioral dimensions.
Copyright (c) 2026 Ning Liu https://creativecommons.org/licenses/by/4.0
Research on the Promotion Strategies of English Intercultural Communicative Competence Transfer in Colleges and Universities Supported by Artificial Intelligence
In order to better cultivate students' cultural adaptability to cope with the challenges brought by globalization, and at the same time to improve the quality of teaching in order to discover more effective teaching methods, the article explores the promotion strategy of English intercultural communicative competence transfer in colleges and universities with the support of artificial intelligence. It proposes a cross-cultural learning resource recommendation model for English in colleges and universities that combines generative adversarial network and collaborative filtering model, and then introduces the conceptual mapping concept to construct an English cross-cultural communicative competence cultivation model from five aspects: theoretical foundation, teaching goal, operation procedure, realization conditions and evaluation. Software concept maps and paper-and-pencil concept maps have different roles in cultivating primary school students' intercultural communicative awareness, knowledge and competence, with significant differences in intercultural communicative awareness and knowledge dimensions (P<0.05). The results of the content analysis and case study indicate that the software concept map is more scalable and facilitates cooperative learning and mutual evaluation among students. The research in this paper is conducive to the cultivation of intercultural communicative competence of college students, and it has advantages in cultivating intercultural communicative competence compared with traditional college English teaching.
Copyright (c) 2026 Zexi Wang https://creativecommons.org/licenses/by/4.0
A study on the comprehensive development of vocal education under the dual role of voice training and mental quality cultivation
The development of modern vocal education presents a diversified trend, in which the application and development of artificial intelligence technology has a positive impact on voice training and mental quality training. In this paper, we first use the average amplitude difference function method to extract the fundamental tone of the music signal, and find that there is a misdetection of the fundamental tone in the process, which is smoothed by using the median smoothing algorithm and the linear smoothing algorithm mixed method, aiming at eliminating the interfering information in the music signal. On this basis, by improving the vocal feature matching of the DTW algorithm, the intelligent diagnosis of voice training in vocal music education is realized, with a view to improving the quality of students' voice training. According to the actual performance of students in the process of voice training, it is proposed to use the Bayesian Knowledge Tracking-based Music Resource Recommendation Model (BKT-ER) to help students master the knowledge points of vocal training, which prompts students to overcome the psychological pressure and bad emotions in playing, and helps to establish a good psychological quality. Finally, from the voice training and psychological quality training program, the integration of the two application program is developed, and an example analysis of the program is carried out. Under the dual role of voice training and psychological quality cultivation in vocal education, students' basic skills, emotional regulation ability, and psychological cognitive ability are effectively improved, and the mean value of its dimensions is greater than 3.5, which fully verifies the effectiveness of the fusion program in this paper.
A study of corporate sustainability strategies in education: an innovative learning model from a data mining perspective
The education industry is experiencing profound changes under the joint promotion of digital economy and lifelong concept. The research combines data mining tools, proposes a multi-indicator segmentation system for customers of education enterprises, constructs a customer segmentation model, analyzes the customer data of education enterprises collected by the improved K-means clustering method, determines the results of customer segmentation, and at the same time, combines the customer segmentation with the collaborative filtering recommendation algorithm to propose an innovative learning model based on the recommendation of customer segmentation. Through the results of the study, it can be seen that the improved K-means clustering method divides the customers into 4 classes, and the innovative learning model of this paper's recommendation algorithm compared with the traditional recommendation algorithm has excellent performance in terms of recommendation accuracy and recommendation time, which proves the effectiveness and practicality of the recommendation algorithm. Finally, the sustainable development strategy of enterprises in the field of education is proposed to provide technical support and theoretical reference for educational enterprises to realize the expansion from scale to refined management and sustainable development.
Copyright (c) 2026 Xiepeng Yue, Kambarova Zhumagul Ularbaevna, Yunning Shao https://creativecommons.org/licenses/by/4.0
Engineering Animated Characters for Cross-Cultural Markets: AI-Assisted Parametric Modeling and Cultural Identity Adaptation
3D animation character modeling is not only time-consuming and labor-intensive, but also has high requirements on the modeler's professional skills. For this reason, this paper proposes a parametric model regression algorithm based on a two-branch structure from the problems existing in animation character modeling. The algorithm obtains the corresponding shape parameters and pose parameters as a priori information by matching the parametric model, so that the implicit function can be effectively sampled and mapped to the point set in the reconstruction. At the same time, the shape parameters and pose parameters of RaBit are regressed separately, and multiple rounds of regression are performed on the pose parameters using the geometric features as reference information in order to realize the fine-tuning of the pose parameters. On the three datasets of Render People, THUman 2.0 and Deep Human, the PSNR values in terms of reconstruction accuracy are 30.83, 29.93 and 31.08, and in terms of rendering efficiency are 64, 48 and 73 FPS, respectively, which achieve a good balance between reconstruction accuracy and rendering efficiency. Finally, the value of animation characters in spreading and transmitting national culture and strengthening cultural identity is discussed, and a cultural identity suitable for the development of domestic animation is constructed, which provides a new direction for animation character reconstruction.
Copyright (c) 2026 Jihong Huang, Shafilla Subri, Faryna Mohd Khalis, Rui Li https://creativecommons.org/licenses/by/4.0
The Music Contextual Teaching Method: A Study on the Practical Approaches of How Musical Elements Facilitate Immersive Learning of Historical Knowledge from an Interdisciplinary Integration Perspective
Music contextual teaching is increasingly emphasized by the educational community. This paper builds a music teaching system architecture for audio processing. Kinect is used to obtain the music teaching scene data, correct the depth camera and color camera, and use OpenNI for coordinate conversion to transform the two-dimensional depth data into a three-dimensional spatial point cloud. The virtual scene 3D modeling data is rendered to generate the free viewpoints of the music teaching 3D classroom. The results show that the method in this paper obtains a good post-alignment positional attitude for the point cloud of the classroom object, the average MAE is only 3.538, and the average alignment time is 31.84 ms. Meanwhile, the classroom reconstruction positional error of this paper's algorithm is reduced by 39.43% on average compared with that of the BundleFusion algorithm. Through the teaching experiment, the historical knowledge achievement using virtual reality teaching improved by 9.23 points on average, which is a significant achievement improvement compared to traditional teaching methods.
Copyright (c) 2026 Zaichao Zhai, Asipova Nurbubu Asanalievna, Chenjie Li, Baoxiang Lu, Yali Huang https://creativecommons.org/licenses/by/4.0
Exploration of the Ecological Wisdom in the Production Techniques of Chaozhou Dancong Tea within the Framework for Protecting Intangible Cultural Heritage of Agriculture
Chaozhou single-compass tea has high production techniques, distinctive product characteristics and good industrial benefits. The study provides a basis for the ecological wisdom mining of Teozhou single-conglomerate tea by sorting out the characteristics of Teozhou single-conglomerate tea industry. The data mining method of tea production skills mainly contains association rules and clustering mining methods. The association rule adopts Apriori algorithm to mine the temporal and causal associations among tea leaves, and find out the item set that satisfies the minimum confidence level as the output of tea association rule. The tea clustering method is optimized on the basis of Density-Based Clustering (DBSCAN) algorithm from spatio-temporal data pooling, density thresholding, K-mean nearest neighbor method, and mathematical expectation method. The results showed that the confidence level of relatively low minimum temperature → large occurrence of pest class = 72.3%, and the confidence level of very high rainfall → medium occurrence of pest class = 98.9%. The largest coefficient of variation among the agronomic traits of Teochew single bush tea varieties was 100 bud weight, with a coefficient of variation of 29.34%. The highest correlation between bud length and width ratio and leaf spreading angle was found, with a correlation coefficient of 0.964. The clustering of Teochew single bush tea was divided into three categories, each of which contained five, four, and three tea tree varieties, respectively. The ecological excavation of Chaozhou single-cong tea provides a cognitive framework for the protection of agricultural non-cultural heritage.
Copyright (c) 2026 Min Zhang, Junlang Huang https://creativecommons.org/licenses/by/4.0
Innovation and Practice of the Ethical Supervision Mechanism for Computer-Assisted Publishing under the Integrated Publishing Model
With the development of artificial intelligence, the ethical regulation of computer-aided publishing is strongly challenged. This paper establishes the process of ethical regulation of computer-aided publishing, calculates the text similarity, and uses the K-Means algorithm to cluster and analyze the subject keywords. The LDA model is established on the basis of Dirichlet distribution to mine the subject features of computer-aided publishing, and the classification key results are based on plain Bayes, so as to discuss the ethical misconduct of computer-aided publishing. The results show that 73.62% of the keywords in the field of computer-aided publishing, which accounted for the largest percentage, belong to verbs. The mean value of similarity of near-synonyms under publishing keywords reaches 0.864, which helps to sort out the association of different attribute word classes. Also the dendrogram of keywords obtained by clustering has the highest distance of 1.15. The computer-aided publishing regulatory mechanism established in this paper helps to adapt to the convergent publishing model.
Copyright (c) 2026 Yongguo Hu, Li Dai, Zonghui Wu, Wenjie Huang, Jie Liu, Yahui Li, Yongsong Yan https://creativecommons.org/licenses/by/4.0
Integrating Heart Rate Variability and Exercise Load for Early Warning of Overtraining Syndrome
Objective To develop an early warning model for overtraining syndrome (OTS) and provide OTS patients with a targeted and practical risk assessment tool, thereby reducing the incidence of OTS among athletes undergoing long-term excessive training and improving the quality of personalized care. Methods Seventy-two patients with OTS were selected. Eight indicators, including training load and heart rate variability, were chosen as model inputs. Relevant risk factors were identified using LASSO regression analysis, and early warning models were constructed using machine learning methods such as logistic regression and gradient boosting classifiers. Model performance was evaluated using metrics such as AUC, accuracy, sensitivity, and specificity, while calibration curves and receiver operating characteristic (ROC) curve analysis were employed to assess the model’s calibration and clinical utility.Results: Variables were screened via LASSO regression analysis, ultimately identifying eight factors—including training load and heart rate variability—as potential risk factors. The logistic regression model demonstrated the best performance (AUC = 0.841; 95% CI: 0.780–0.898) and demonstrated good clinical utility in both calibration curve and decision curve analyses. Conclusion: The OTS early warning model developed in this study facilitates the long-term, systematic collection of physiological data from athletes undergoing prolonged excessive training, promotes the clinical translation of early warning models, screens for high-risk OTS patients, guides nursing practice, and effectively reduces the incidence of OTS.
Copyright (c) 2026 Yueguo Jia https://creativecommons.org/licenses/by/4.0
Hybrid Teaching Feedback Mechanism and Efficiency Improvement Paths for Students’ Engagement in College English Learning in Guangdong Based on Learning Analytics
Blended teaching mode has become the mainstream English teaching mode in Guangdong colleges and universities, how to make the communication between teachers and students more timely and in place is an important part of whether the blended teaching mode can give full play to its advantages. This paper generates a personalized knowledge point collection based on the knowledge point organizing strategy, then uses a neurocognitive diagnostic model to obtain students' English cognitive level, and establishes a feedback mechanism for blended teaching of English in colleges and universities based on students' level with the help of the DKVMN model. On this basis, in order to reveal the path of student engagement enhancement under the blended teaching feedback mechanism, the structural equation theory and scale testing method were chosen to construct the student engagement model. After the calculation of DKVMN model, we get that English topic 1914 has high correlation with knowledge points 10, 11, 13, 14 and 4, while English knowledge point 18 has almost no correlation, which accurately detects the English knowledge points that students have not mastered, and then provides students with targeted review methods, which verifies the effectiveness of the feedback mechanism. In addition, the path coefficient of self-efficacy and engagement is 0.206, and the other potential variables are similar, which comprehensively visualizes the quantitative relationship between the paths of students' engagement augmentation under the feedback mechanism.
Copyright (c) 2026 Xiao Jin, Syamsul Nor Azlan Mohamad, Norhayati Mohd Yusof, Xian Liu https://creativecommons.org/licenses/by/4.0
A Practical Study on the Integration of Learner Profiling and Teachers' Digital Literacy in Foreign Language Classrooms Driven by Big Data
The development of big data technology provides new opportunities for the development of education and gives new connotations and missions to precision teaching, and the integration of learner portraits and teachers' digital literacy practices in foreign language classrooms is now studied using college English classroom teaching as an example. Based on the learning data in the foreign language classroom, the study constructs individual portraits of learners in the foreign language classroom in terms of learner styles, learner behaviors and learner outcomes, and generates group portraits by using the K-means algorithm based on Canopy and maximum-minimum distance to output the learner portraits in the foreign language classroom. The results of the learning portraits are then embedded into the teaching process, realizing the accurate teaching practice that is organically integrated with teachers' digital literacy. The foreign language classroom presents four types of learners: "potential learners", "excellent learners", "borderline learners" and "striving learners", with their respective proportions being 46.00%, 19.80%, 13.40% and 20.80%. Teaching practice shows that the method not only improves students' learning ability and interest in learning, but also improves students' innovation and thinking ability.
Copyright (c) 2026 Lixia Jing https://creativecommons.org/licenses/by/4.0
Research on the Algorithmic Interpretation and Dissemination Mechanism of World Heritage Values from the Perspective of Digital Humanities
World heritage value is the core of heritage protection and inheritance, and how to communicate the value of world heritage effectively is a major issue in the current world heritage protection and inheritance. Based on the perspective of interpretation and creation of world heritage value, this study constructs a world heritage value co-creation model based on blockchain PBFT algorithm, and designs a Dunhuang cultural heritage intelligent data resource management system based on this model. Meanwhile, using structural equation modeling, the influencing factors of world heritage digital communication are explored, and the design of world heritage digital communication mechanism is realized. It is shown that the ML-PBFT algorithm proposed in this paper outperforms the standard PBFT algorithm in terms of transaction delay, throughput and communication overhead. Compared with the traditional centralized crowdsourcing system, the system in this paper has obvious advantages in crowdsourcing matching, task result auditing and crowdsourcing data authorization sharing. In addition, the characteristics of the digital communication media of intangible cultur heritage and the audience perception factors can significantly and positively influence the audience's behavioral willingness, and the user's perception plays a partly mediating role in this study.
Copyright (c) 2026 Shangqian Wu, Suresh Narayanen https://creativecommons.org/licenses/by/4.0
The Impact of Physical Education Teachers’ Digital Teaching Competencies, Empowered by Smart Education, on Student Participation in Physical Activity: A Time Series Analysis
With the rapid development of information technology and intelligent systems, smart education has emerged as a key direction in modern education, providing both technological support and practical pathways for cultivating core physical education and health competencies. In light of this, this study utilizes statistical data on physical education teachers’ digital teaching capabilities and students’ physical activity participation from 2010 to 2025 in a certain city. It establishes a vector autoregressive model between physical education teachers’ digital teaching capabilities and students’ physical activity participation, and employs unit root tests, cointegration analysis, impulse response functions, and variance decomposition to analyze and predict the interactive dynamics and time-series relationships between the two. The results indicate that a bidirectional Granger causality relationship exists between physical education teachers’ digital teaching capabilities and students’ physical activity participation. The contribution rate of students’ physical activity participation to changes in physical education teachers’ digital teaching capabilities is relatively high in the short term and then gradually decreases. The contribution rate of physical education teachers’ digital teaching capabilities to fluctuations in student physical activity participation showed a rapid upward trend after the second period, reaching 72.08104% by the eighth period. The variance contribution rate of physical education teachers’ digital teaching capabilities was significant and exhibited a continuous upward trend over time.
Copyright (c) 2026 Xiaofeng Zhu, Liang Shang, Shengchao Du https://creativecommons.org/licenses/by/4.0
Who is Leading the Housing Satisfaction Study: A Bibliometric Analysis based on Bibliometrix Tools and CiteSpace
Due to its significant implications for individual well-being, community cohesion, and socio-economic development, housing satisfaction has increasingly become a critical topic in urban studies, sociology, and public policy. In 2023 alone, 149 articles related to housing satisfaction were indexed in the Web of Science Core Collection (with a cumulative total of 1,383 articles as of August 15, 2024). While these contributions enrich the research field, they also present challenges of information overload and difficulty in filtering relevant studies for application. Consequently, this study employs bibliometric methods to analyze the performance of existing research, focusing on key actors and influential works from five perspectives: country/region, institutions, authorship, publication sources, and citation metrics. Additionally, it categorizes the main branches of housing satisfaction research based on terminological standards to assist stakeholders in quickly identifying potential opportunities for support, collaboration, and citation. The results indicate that the United States and China dominate the field nationally, with institutions such as the Ohio University System and Hong Kong Baptist University forming the core research collaboration networks. At the author level, Hur Misun, Mouratidis Kostas, and Lu Max emerge as the most influential authors in terms of cumulative impact, short-term influence, and citation frequency, respectively. In terms of research branches, the most concentrated topics include residential satisfaction, housing satisfaction, and neighborhood satisfaction. The study also reveals that the research collaboration network in this field is relatively fragmented, with a reliance on traditional data sources and methods, and an overly concentrated distribution of research topics
Copyright (c) 2026 Ying Li, Chin-Hong Puah, Hamrila Abdul Latip https://creativecommons.org/licenses/by/4.0
The Impacts of Climate Change on Temperate Forests in Europe and Adaptation Options
Vegetation phenology is a sensitive indicator of climate change, and its changes directly reflect the response of vegetation growth to climate change. The study of vegetation phenology is of great significance to understand the impact of global climate change on terrestrial ecosystems. In this paper, the impact of climate change on temperate forests in Europe was investigated using biased correlation analysis and multiple regression analysis based on NDVI extracted vegetation phenology parameters and climate data from the German Weather Service (DWD) observation network in the Hartz Mountains forest study area. It was found that the interannual variability of forest phenology in the Hartz Mountains from 2001 to 2020 was characterized by summer and fall temperatures as well as fall precipitation as the main factors affecting EOS in the study area. Changes in the sensitivity of forest vegetation EOS to temperature and precipitation were strongly correlated. The effects of both temperature and precipitation on forest EOS showed a trend from negative to positive, in which every 1 mm increase in precipitation from April to August would advance the forest by 0.15 d on average. The correlation between NPP and temperature and precipitation was gradually significant with the increase of time scale. In order to cope with the problem of climate change, it is reasonable to meet the urgent requirements of climate change adaptation by improving the corresponding forest legal system.
Copyright (c) 2026 Yuan Chen https://creativecommons.org/licenses/by/4.0/
A Digital Twin and Diffusion Model Integrated Method for Power System Operational State Generation and Risk Assessment
The integration of renewable energy sources and the increasing frequency of extreme weather events have introduced significant uncertainties into power system operation, challenging traditional risk assessment approaches that rely on limited historical data. This study proposes a novel method that integrates digital twin technology with diffusion models for operational state generation and risk assessment in power systems. The digital twin framework constructs a high fidelity virtual replica of the physical power grid, enabling real time simulation of system dynamics under diverse operating conditions. The diffusion model, specifically a conditional denoising diffusion probabilistic model, is employed to generate realistic and diverse operational scenarios, including extreme events such as high renewable variability, load peaks, and cascading failure conditions. These generated scenarios are subsequently fed into the digital twin simulation platform to assess system risks quantitatively using probabilistic metrics. The method is validated on the IEEE 39 bus and IEEE 118 bus test systems using publicly available operational datasets. Experimental results demonstrate that the proposed approach generates high quality scenarios that capture both typical and extreme operational patterns, achieving superior performance in risk identification and decision support compared to conventional generative methods. This study contributes a comprehensive framework that bridges generative artificial intelligence and physical simulation for enhanced power system resilience.
Copyright (c) 2026 Shaohan Liu https://creativecommons.org/licenses/by/4.0
Asset Risk Pricing with Causal Representation Learning and Generative AI: Evidence from Complex Market Environments
This study examines asset pricing in high-dimensional financial environments, where traditional factor models face limitations in interpretability and stability, and machine learning approaches remain largely correlation-driven. Existing methods often struggle to identify economically meaningful risk factors and exhibit limited robustness across changing market conditions. To address these limitations, this paper proposes a unified framework that integrates generative modeling with causal representation learning. A generative model is first employed to extract latent structures from high-dimensional financial data, and a causal learning module is subsequently used to identify invariant factors based on structural relationships. These factors are then incorporated into a standard asset pricing model using the Fama–MacBeth regression approach. Empirical results show that the proposed model achieves a cross-sectional R2 of 0.38, compared to 0.26 for the Fama–French five-factor model and 0.31 for a deep learning benchmark. Pricing errors are reduced from 0.054 (FF5) and 0.049 (deep learning) to 0.036. In addition, the model maintains relatively stable performance across different market conditions, including bull, bear, and crisis periods, while benchmark models exhibit a more pronounced decline in explanatory power. These findings suggest that incorporating causal representation improves both explanatory power and robustness in asset pricing. By linking latent structure learning with causal invariance, the proposed framework provides a structured interpretation of risk premia and offers a more stable foundation for asset pricing in complex market environments.
Copyright (c) 2026 Ao Zhang https://creativecommons.org/licenses/by/4.0
Dynamic User Modeling and Lightweight Knowledge Signals for Candidate Reranking: A Reproducible Two-Stage Study
This study investigates whether introducing dynamic user information and simple knowledge signals can improve the re-ranking effect of the fixed candidate list in the recommendation system when computing resources are limited. To this end, we have constructed a reproducible two-stage recommendation framework: the first stage uses an ID-based collaborative retrieval model to generate the candidate item list, and the second stage utilizes a lightweight model to re-sort these candidates, combined with dynamic user modeling as well as semantic and knowledge-related features. We conducted experiments on the MovieLens-1M dataset and adopted a data setting based on user time sequence, evaluating the sorting ability of the second-stage model on the fixed candidate set. The experimental results show that, compared with the lightweight static ID re-ranking baseline, the dynamic re-ranking model has improved in terms of Recall@10, NDCG@10, and MRR@10 indicators. The ablation results indicate that adding more model components does not necessarily lead to better generalization ability. Some simplified configurations perform better on the test set, and the contribution of dynamic user modeling is positive but relatively limited. Further analysis also reveals that this method still faces significant difficulties in recommending rare items, but the efficiency evaluation shows that the entire process is feasible under moderate hardware conditions. Overall, this paper proposes and validates a two-stage candidate re-ranking framework, and through experimental analysis, demonstrates the role of different components in the fixed candidate recommendation scenario.
Copyright (c) 2026 Ruizhi Xu https://creativecommons.org/licenses/by/4.0
The Facilitating Mechanism of Tang Calligraphy on the Formation of Shodo Style in Japan during the Heian Period in the Framework of Cross-cultural Exchange
Tang calligraphy, as the southern part of East Asian calligraphy, had a profound influence on the formation and evolution of Shodo styles in Japan during the Heian period. In the context of cross-cultural communication, the study is to organize and complete the calligraphy style dataset including Tang Dynasty calligraphy images and Japanese Shudo images. The stack self-encoder in deep learning algorithm is used as the feature extraction algorithm, and the Softmax classifier is used for style recognition, and the recognition test is conducted on the expanded dataset for the characteristics of the five calligraphic Chinese character styles. On this basis, the dual influence of Tang Dynasty calligraphy on the aesthetics and style of Japanese Shudo fonts is further explored. The experimental results show that the recognition accuracy of this paper's method on five calligraphic Chinese character styles reaches 98.95%, and the recognition accuracy on cursive and running script styles is significantly higher than that of other recognition methods, which proves the validity of this paper's method on the recognition of calligraphic styles. At the level of influence, Tang Dynasty glyphs (which are one of the factors influencing the foundation of Japanese Shudo aesthetics), replacement lines and unified structures have a significant impact on Japanese Shudo font styles.
Copyright (c) 2026 Yang Wang, Ringah anak Kanyan, Ge Kong https://creativecommons.org/licenses/by/4.0
An Analysis of the Multimodal Representation and Linguistic Mechanisms of the Principle of (Im) politeness Reciprocity in Social Media Interactions
Impoliteness is a negative attitude towards a specific behavior in a specific context, and an impolite strategy is a reflection of the type of impolite output. In this paper, we propose a model for impoliteness detection in social media interaction based on sentiment-dependency graph convolutional neural network with modality fusion from the perspective of pragmatics. The model enhances the emotional and syntactic information of text modality through emotion graph and syntactic dependency graph, uses graph convolutional neural network to obtain text information with rich emotional semantics, and then fuses multimodal features by modal fusion, and filters the redundant information by using the self-attention mechanism to detect impolite reciprocity based on the fused information. Based on the real corpus of Chinese and English blog discourse collected in real time, we study the similarities and differences of impolite language in online discourse between the two languages. The experimental results show that the accuracy of the model reaches 84.72, which is 0.64 percentage points higher compared to the better of the comparison models. Under similar online communication contexts, the formation of overall discourse features and local distinctive features of the two corpora stems from the combined effects of dynamic and diverse online communication contexts and communication resources as well as different linguistic and cultural contexts. The study provides valuable new discoveries for the study of social media online discourse features and enlightens people's comprehensive understanding of online language and functions.
Copyright (c) 2026 Renjun Pan, Xiaodong Wang https://creativecommons.org/licenses/by/4.0
Social-environmental interventions for professional development geared toward sustainable education: the mediating role of pedagogical self-efficacy in enhancing physical education literacy teaching for early childhood teachers
Under the framework of sustainable education, improving the physical literacy teaching ability of kindergarten teachers is of great significance in promoting children's comprehensive development, and teaching self-efficacy was introduced as a mediating variable. A stratified random sampling method was used to conduct a questionnaire survey on 520 kindergarten teachers in Shijiazhuang city district and the surrounding districts and counties in Hebei Province, which was analyzed by using stepwise regression with Bootstrap mediation effect test. The results showed that the mean score of kindergarten teachers' career development social environment was 4.05, the mean score of teaching self-efficacy was 4.26, and the mean score of physical education literacy was 4.19, and there was a significant positive correlation between kindergarten teachers' career development social environment, physical education literacy, and teaching self-efficacy. The social environment of early childhood teachers' career development significantly predicted their physical literacy, and after teaching self-efficacy entered the regression equation, the regression coefficient of physical literacy decreased, but its significance level still existed. Early childhood teachers' physical literacy was influenced by both career development social environment and teaching self-efficacy, and teaching self-efficacy played a partial mediating effect between early childhood teachers' physical literacy and career development social environment.
Copyright (c) 2026 Shijiao Li, Denise KOH https://creativecommons.org/licenses/by/4.0
Research on the Infiltration and Inheritance Strategy of Chinese Traditional Music Culture in Higher Vocational Vocal Music Teaching
The practical application of traditional music culture in higher vocational vocal teaching can not only stimulate students' interest in vocal learning and give them a more comprehensive understanding of vocal knowledge, but also help students improve their own vocal perception ability and comprehensive vocal literacy. In this paper, oriented to the field of traditional music culture, we design and validate the named entity recognition algorithm based on the BERT-BILSTM-CRF model and the entity relationship extraction model based on the BERT-BiLSTM-Att to complete the visualization of the creation of the knowledge map of Chinese traditional music culture. On this basis, the online-offline hybrid vocal music teaching mode for higher vocational education that integrates the knowledge graph is constructed, and teaching experiments are conducted to investigate the effect on the inheritance of traditional Chinese music culture. The experiment shows that after the intervention of using the vocal music teaching method incorporating traditional music culture knowledge mapping, the students' mastery of traditional music culture knowledge, traditional music culture literacy, and vocal singing ability are significantly improved (P<0.05), and are better than the traditional teaching method, i.e., the method helps students to improve their own vocal music perception ability and comprehensive vocal music literacy. Based on this study, it is expected to help improve the teaching level of vocals in higher vocational education and the inheritance and development of Chinese traditional music culture.