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Research on student achievement evaluation and management mechanism based on support vector machine

Student achievement evaluation is of great significance for supervision and guidance of teaching management. However, the traditional method of evaluation and analysis based on absolute score has some defects, which can not fully reflect the information contained in student achievement, so that the impact of student achievement evaluation on teaching management is limited. To solve this problem, this paper uses support vector machine (SVM) to study students' performance, takes students' usual performance as the input of the model, and the performance label as the output of the model. And through empirical analysis, it verifies the importance of the intrinsic information hidden in the achievement data to evaluate students' achievements, thus providing a favorable basis for teaching management. Using scientific methods to predict students' grades can not only guide students' targeted tutoring, but also provide scientific decision-making basis for teaching administrators.