Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Abstract

Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.

Unmasking the challenges in ideological and political education in China: A thematic review

“…Some of the research highlights the importance of integrating civic and mental health education to optimize teaching methods and promote students' holistic development [ 27 ]. Some studies propose multi-channel-based fusion models, utilizing technologies such as BERT, CNN, BiLSTM-Attention, and fine-grained parallel computing programming to effectively analyze text and improve mental health education models [ 27 , 28 ]. And some of the studies emphasized the use of AI for emotional analysis and teaching methods in ideological and political education such as [ 29 ] examined the emotional attributes of ideological and political education, proposing deep learning models that combine GRU, attention mechanisms, and BERT to improve emotion analysis [ [30] , [31] , [32] , [33] , [34] ].explored intelligent teaching methods, facial expression recognition, sentiment analysis, and parameter optimization in the context of ideological and political education.…”