LAUSR

This paper aims to design and implement the online political and ideological teaching system based on personalized recommendation in order to more accurately recommend teaching resources appropriate for students' learning, thus improving the learning efficiency and teaching quality of the online political and ideological teaching system. First, the design of the online political and ideological education system is detailed, along with its basic framework, functional modules, hierarchical structure, and database. A personalized recommendation approach based on knowledge map is proposed. The algorithm is applied to the online political and ideological teaching system to understand the differences of students' interests in different teaching resources, establish a student interest transfer model, and effectively improve the transfer of students' interests. On the basis of knowledge map, the matrix decomposition method is introduced, matched with the knowledge map to obtain the recommendation prediction score, and the feedback model is established and extended. Measure the dynamic transformation of the recommended ideological and political teaching content, and comprehensively consider the long-term and short-term preferences of students, so as to realize the personalized recommendation of ideological and political teaching resources. Experiments show that the personalized recommendation online political and ideological teaching system designed in this paper has good overall performance, the accuracy of the proposed recommendation approach is high, and the recommendation time is fast, so as to improve the teaching quality of the teaching system.