The Application of data Mining Technology in Student Management

In order to understand the application of data mining technology in student management, the author proposes an application research based on data mining technology in student management. The author first introduces big data technology into university management work, opens up information channels between multiple business systems, and establishes a university data lifecycle management model. Secondly, the KNN classification algorithm in data mining is used to mine a large amount of data from students’ online activities. Finally, the relationship between traffic volume, duration, and student grades is found, and abnormal data student behavior is intervened, which is applied to student behavior management. The credit passing status of students was compared based on traffic trends. The experimental prediction results show that the accuracy, precision, and recall rates of abnormal prediction are 74.25%, 77.65%, 81.23%, and the F-Score value is 77.56%, respectively. The use of data mining technology has become an important way to solve this problem.