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Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation

Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation

Xi Zhang
Copyright: © 2022 |Volume: 14 |Issue: 3 |Pages: 15
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781683180326|DOI: 10.4018/IJISSS.311861
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MLA

Zhang, Xi. "Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation." IJISSS vol.14, no.3 2022: pp.1-15. http://doi.org/10.4018/IJISSS.311861

APA

Zhang, X. (2022). Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation. International Journal of Information Systems in the Service Sector (IJISSS), 14(3), 1-15. http://doi.org/10.4018/IJISSS.311861

Chicago

Zhang, Xi. "Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation," International Journal of Information Systems in the Service Sector (IJISSS) 14, no.3: 1-15. http://doi.org/10.4018/IJISSS.311861

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Abstract

The enrollment expansion of colleges and universities and the acceleration of today's social process bring fierce competition to students. Colleges and universities should attach great importance to the mental health and employment anxiety of graduate students. In order to better explore the relationship between them, this paper uses artificial intelligence (AI) to evaluate students' psychology. The results show that: (1) When the crossover probability P value is less than 1, the psychology tends to be stable, and the emotion simulation conforms to the law of emotion change. (2) The accuracy of this model is higher than 82%, and the weighted fuzzy reasoning method can effectively analyze psychological symptoms. (3) After iteration, CNN has different recognition degrees for six emotions. (4) Finally, according to the emotional analysis given by the model, the source of students' psychological problems is discussed, and it is found that these students have different degrees of bad academic behavior; while they are anxious about employment, the employment rate is not satisfactory.

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