Citation

Yang L. Wireless Communications and Mobile Computing 2022; 2022.

Copyright

(Copyright © 2022)

DOI

10.1155/2022/3719882

PMID

unavailable

Abstract

Since entering the 21st century, my country's education is facing the transition from traditional exam-oriented education to quality education and personality education, and the problem of mental health has gradually attracted the attention of all sectors of society. Suspension, drop-out, suicide, and crime occur from time to time, and the number of students with psychological disorders is also increasing year by year. Therefore, in-depth research on the mental health of vocational college students and exploration of students' psychological intervention models have become the focus of colleges and other education departments. For the focus of research works, the application of DM technology in the field of teaching and management in an institution of higher learning has also achieved initial results. Through consulting a lot relevant literature, this paper deeply studies the DM technology and tries to analyze the application of DM technology in the data of psychological problems of higher vocational students. Through experimental comparison, a single algorithm model is used to classify and predict the test data set, and the accuracy rate is 78% after comparing the classification results with known categories. The accuracy of classification results obtained by the fusion algorithm in this paper is 85.1%. The results show that this technology provides help for students' mental health counseling, mental health education and the prevention of students' psychological problems in higher vocational colleges. It provides new ideas and methods to solve the mental health problems of higher vocational students and makes the school mental health education more purposeful, targeted, and effective. © 2022 Lingfei Yang.

Language: en

Keywords

College students; Students; Mental health; Health; Drop-out; Psychological disorders; Data mining; Classification results; Classification (of information); Mental health educations; Education computing; Engineering education; Higher vocational colleges; Quality education; Statistical tests; Student-based; Suspension drops