New Directions and Development Models for College Student Education based on Machine Learning Classification Algorithms

For the contemporary student teaching system, the information-based education management system has played a positive role in the rapid promotion of teaching. In order to get the teaching exploration data of modern information management from the data increment on the Internet, this paper updates the technology of data mining. This paper constructs a quantitative table for teacher positions and conducts ideological and political job evaluations in the database. This paper mainly focuses on the management and construction of ideological informatization based on the [Formula: see text]-means algorithm. By using the [Formula: see text]-means algorithm to analyze the daily behavior data of students, we can discover their behavior patterns and trends, providing decision support for student management. For example, by analyzing data such as students’ internet time, social activities, and library borrowing records, we can understand their interests, hobbies, and learning habits, providing a foundation for personalized education. Cluster analysis of students’ ideological and political performance using the [Formula: see text]-means algorithm can identify student groups with different ideological tendencies. This helps schools to carry out targeted ideological and political education work, and improve the pertinence and effectiveness of ideological and political education. Through quality tracking analysis of educational evaluation, it has carried out educational informatization tracking management using an information model. In the process of judging and analyzing different subjects, it utilized the hot topics of universities for a more comprehensive information intelligent tracking management.