Impact of WI-FI on college students’ mental health: A deep learning approach

Abstract

The period of college students is an important life stage in the transition from teenagers to youth, and it is the period with the most violent physiological and psychological changes in the growth of whole life. WI-FI has brought many positive changes to the lifestyle of college students, and has also had many negative effects on college students. Under the WI-FI environment, college students’ health is worrying. The number of “head-downers” on campus has increased sharply, and the phenomenon of dependence on the WI-FI has become more and more serious, which seriously endangers the health of students. In view of this, this paper conducts an in-depth analysis in the WI-FI environment, hoping to understand its specific impact and propose effective solutions based on its existing problems. By analyzing the impact of WI-FI environment on college students’ physical and mental health, find out the specific types of morbidity caused by WI-FI environment on college students’ physical and mental health. The data of a college student group in a university are collected to establish a judgment model and recognition model based on deep learning. The model integrates convolutional neural networks (CNN) and machine learning algorithms to comprehensively assess the health status of college students. This initiative helps to better implement the guiding principle of “health first” in school education and promotes campus culture construction.