The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

Some factors affecting the physical and mental health of vocational college students, the sense of inferiority plays a very important role in cultivating students with physical and mental health. Inverse random under sampling algorithm is improved based on integrated learning, which can improve the performance of the classifier. Stacking integrated learning and flip random sampling reduction algorithm SIRUS is proposed. Select the individual subjective factors studied in this paper is important in self-attribution and social objective factors are important social support factors, and the only demographic variables is a significant difference.