Data Mining and Feature Analysis of College Students’ Campus Network Behavior

The rise and promotion of big data methods enables teachers to understand the behavior patterns of students in a timely and accurate manner, especially to find out the groups of students that need to be focused on in time, and to help promote the student affairs management from empirical qualitative knowledge to scientific quantitative analysis. This paper applies the clustering method of data mining to analyze the campus network behavior of 3,245 students in a certain grade of B university, obtains a total of 23.843 million Internet access data in 4 years. The result shows 4 groups of students with different characteristics of Internet access, finds 350 students with large network usage. Achievements and other aspects of performance of these students are affected. This study carried out data mining of student campus network behavior, which can be used as a practical operation case for student affairs management data mining, providing effective data support for the accurate and scientific development of student affairs management.