Artificial Intelligence Enabling Civic Education in Colleges and Universities: Synergistic Operation Strategies of Red Culture Dissemination and Enterprises

Abstract

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.