The Impact of the Teaching Mode of Physical Activities Independent Courses in Arts Education on the Physical Changes and Mental Health of College Students Based on Deep Learning Analysis



Deep learning; Mental health; Tunicate swarm optimization; College students; Teaching mode; Physical activities


Purpose: This study presents a novel Tunicate Swarm Optimisation Algorithm with Deep Belief Network (TSOADBN) model for assessing the influence of the instructional approach in arts education on the mental well-being of college students. In order to achieve this objective, the TSOADBN model primarily examines the input data in order to identify the patterns associated with the mental health condition. Design/methodology/approach: A series of simulations were conducted to demonstrate the improvement of the TSOADBN model, and the experimental findings were examined using multiple metrics. Findings: The experimental findings demonstrated that the TSOADBN model exhibited superior outcomes compared to alternative models. The prevalence of mental disorders is on the rise within the populations of developed nations in the Western industrialised world. There exists a robust correlation between physical well-being and mental well-being, albeit the mechanisms underlying the interplay between the two are relatively recognised. Originality/value: Upon examining the research conducted on co-founders, it was found that previous studies have primarily focused on the significant interplay between mental and physical health. However, there is limited knowledge regarding the various potential mechanisms through which mental health impacts physical health and vice versa (i.e., known as “indirect effects”). Therefore, this study addressed a significant void in the existing body of literature. The TSOADBN model, as proposed, primarily utilises the DBN classification model to analyse the input data effectively. The TSOA is utilised to select the associated hyperparameters in order to optimise its performance effectively.