The analysis of generative adversarial network in sports education based on deep learning

• Published in: Scientific reports Vol. 14; no. 1; pp. 30318 - 14
• Main Authors: Eerdenisuyila, Eerdenisuyila, Li, Hongming, Chen, Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.12.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 639/705/1046; 639/705/117; 639/705/258; 639/705/531; 639/705/794; Adaptability; Colleges & universities; Deep Learning; Education; Generative Adversarial Network; Humanities and Social Sciences; Humans; Mental Health; multidisciplinary; Neural Networks, Computer; Quality evaluation; Quality of education; Reward function; Science; Science (multidisciplinary); Sports - education; Students; Students - psychology; Universities; University students; Deep learning; Generative Adversarial Network; Quality evaluation; Reward function; Mental health
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-81107-5

The importance of mental health is increasingly emphasized in modern society. The assessment of mental health qualities among college and university students as the future workforce holds significant significance. Therefore, this study, aiming to streamline the process of writing quality evaluations and enhance the fairness of assessment comments, explores the use of Generative Adversarial Network (GAN) technology in deep learning to evaluate the mental health qualities of college and university students through the unique avenue of sports. Firstly, GAN and Sequence Generative Adversarial Network (SeqGAN) models are introduced. Secondly, GAN is employed to construct a model for generating evaluation texts, encompassing the construction of a generator and discriminator, along with the introduction of a reward function. Finally, the constructed model is utilized to train on evaluation texts related to the mental health qualities of college and university students engaged in sports, validating the effectiveness of the model. The results indicate: (1) The pre-training of the generator in the constructed text generation model stabilizes after the 10th epoch. In contrast, the pre-training of the discriminator gradually stabilizes after the 35th epoch, demonstrating overall good training effectiveness. (2) When the generator’s update speed surpasses that of the discriminator, the model’s loss does not converge. However, with a reduction in the ratio of rounds between the two, there is a noticeable improvement in the convergence of the model. (3) The mean score of adaptability quality is the highest among the four indicators, suggesting a strong correlation between comment generation and adaptability quality. The results validate the effectiveness of the proposed text generation model in semantic control. This study aims to advance the level of mental health education among college and university students in the sports domain, providing theoretical references for enhancing the effectiveness of quality education assessments in other subjects as well.