Design of Psychological Well-Being Education Environment Scheme Based on Deep Learning Theory.

This paper discusses the structure of psychological well-being education programmes in higher education institutions based on an analysis of the connotation and characteristics of deep learning theory, as well as the background of today's talent training requirements, the psychological traits of contemporary students, and the practical requirements of the teaching reform of psychological well-being education courses in higher education institutions. A model for evaluating the psychological well-being of college students based on BPNN is presented in this paper, which also addresses the current severe shortage of full-time psychological counsellors. Additionally, the traditional BPNN is optimised by GA, and the resulting NN can better achieve the desired effect, demonstrating the viability of BPNN. It enables the psychological well-being of college students to be self-diagnosed online and significantly lessens the workload of psychological counselling institutions in higher education. According to the experimental findings, the optimised algorithm's accuracy can reach 92.47 percent, and it is considered to be reliable. This study not only offers a novel approach to nonlinear data processing, but also paves the way for variable screening in the presence of an ambiguous structure. Additionally, in a limited sense, it offers insightful research for psychological education in higher education institutions.