Proper

Abstract

With the rapid development of education informatization in recent years, the data of the education industry has also increased rapidly. How to discover the potential value of the data from these complex and massive data, improve the teaching methods of education, and improve the comprehensive quality of students is a big issue for the education industry A must for data development. In this paper, K-means algorithm is used to cluster a large amount of data from various applications in the digital campus data sharing library of GY universities. Using the K-means clustering algorithm to analyze the student’s consumer behavior, five types of student consumption are obtained. By clustering the student’s life behavior, three types of student groups are obtained. Get a group of students with four characteristics of learning habits. Based on the clustering results, these different types of students are analyzed in detail, and teachers and schools are provided with some meaningful suggestions based on their characteristics.