Ideological and political education has developed into the era of informationization and technologization, and should consciously follow the changes in the social environment to make corresponding adjustments. Based on big data technology, this paper shifts the development of ideological and political education activities to a targeted and fine implementation. Combined with the actual situation of student management in colleges and universities as well as college students’ civic education, a quantifiable student information model is constructed and a personalized learning resource recommendation system is established through data collection, cleaning and normalization. The system adopts user-based collaborative filtering recommendation algorithm to recommend personalized learning resources, and then adopts content filtering recommendation algorithm to optimize the recommendation system for the problems of sparse scoring and “cold start”. After testing, the similarity of collaborative filtering and contentbased similarity have the same weight, and the error value is minimized when the number of recommendation lists is 10. The algorithm of this paper is applied to the personalized learning resources recommendation system for teaching practice, compared with the effect of other algorithms, the students’ performance under the method of this paper is significantly improved, and the students are satisfied with the effect of using the system.
