Algorithmic Research on Integration and Optimization of Civic and Political Teaching Resources of University English Courses under Digital Transformation

Abstract

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%.