Enhancing College Students' Mental Health via PE Teachers: A Study With Multi-Modal Data Fusion Computing Model in the Epidemic

Jian Dang, Min Zhao and Bei Zhao
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Jian Dang: Henan Medical University, China
Min Zhao: Henan Medical University, China
Bei Zhao: Xinxiang Institute of Engineering, China

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2025, vol. 20, issue 1, 1-25

Abstract: During the COVID-19 pandemic college students faced significant pressures impacting their mental health. This article explores the psychological challenges encountered and presents strategies whereby physical education teachers can support students' mental well-being. A deep learning approach was employed using a Convolutional Neural Network-Recurrent Neural Network model for intelligent psychological state evaluation, focusing on textual data analysis related to metaphor usage and the reflection of students' mental states. This study demonstrated that the prediction accuracy for core psychological factors exceeded 90%, showing substantial improvement when compared with traditional single-factor predictions. The findings indicated that this algorithm effectively identified mental health issues, therefore offering valuable theoretical guidance for teachers in helping students regulate their psychological states. Additionally, this research underscored the critical role of physical education in fostering mental resilience among students, particularly during challenging periods, such as the pandemic.

Date: 2025
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