Health Education of University Students Based on Deep Learning

Abstract

Introduction: Students’ ability to recognize mental illness as well as their ability to combat it by dealing with psychological difficulties can be improved with a global understanding of mental health. Lectures and paper flyers have proved to be ineffective for this learning, giving rise to the need to improve the means of health education as this education combined with mental health services can improve students’ quality of life, family happiness and general society. Objective: This study aimed to examine the physical mental health of university athletes, with data processing in an intelligent sports training system based on deep learning. Methods: Student data were collected and divided into two categories. The research group received deep learning, while the control group participated in traditional athletic training. The Clustered Genetic Deep Neural Network for sports training (CG-DNN) was used. ANOVA and Student’s t-tests were applied to assess the students’ mental health. Results: The proposed physical activity can reduce stress, regulate mood, promote mental health, prevent and treat mental illness. Conclusion: Physical exercise developed with deep learning technology is indicated to improve the physical health and regulate the mental health of university students. Level of Evidence II; Therapeutic Studies - Investigating Treatment Outcomes.