Data Mining Algorithm for College Students’ Mental Health Questionnaire Based on Semisupervised Deep Learning Method

Abstract

In recent years, there are many cases of college students who have psychological problems affecting their studies, dropping out of school, and even committing suicide. College students, as a part of high-level talents, have always been regarded as outstanding members of society. They default to have strong psychological qualities, but the reality is disappointing. Various pressures such as academics, social relations, and employment make college students the mental exhaustion has led to many bloody tragedies. The timely detection of psychologically abnormal students has become one of the most concerned and thorny issues in major universities. By constructing a mental health state perception model for college students and optimizing the model parameters, it can be seen that the $f$ score of the internal and external tendency model has increased by 3.3%, the $f$ score of the depression binary model has increased by 2.5%, and the anxiety binary model of the $f$ score has increased by 2.5%. The score increased by 8%. The established model has an obvious effect and can quickly analyze the difference between the behaviors of psychologically abnormal students and normal students in school and also provide a management decision-making basis for college student managers and psychological counselors.