Design of student behavior characteristics analysis platform based on K-means clustering algorithm in local universities

Student behavior characteristic data can fully reflect the behavior law of college students, which is of great significance to the educational management and decision-making of local universities. With the gradual acceleration of information construction in colleges and universities, most colleges and universities have developed the campus information management system. Facing the massive campus management data, how to effectively mine valuable information from it has become an important content to improve the level of student information management. This paper uses big data processing and analysis technology to comprehensively analyze the current big data of local universities. Moreover, a distributed computing platform based on K-means clustering algorithm is constructed based on the behavioral characteristics of college students, and a prediction model between student behavior and talent quality cultivation is constructed. This research can guide teaching management, student management, employment guidance and other related work in local universities.