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

Abstract

Psychological education is beneficial in maintaining the psychological health of college students, resolving psychological issues, and creating a harmonious campus and society. Mental health education for college students is a development-oriented basic education activity that contributes significantly to educational quality. To improve the accuracy of college students' mental health assessments, a model for mental health education for college students based on fine-grained parallel computing programming is proposed. This study uses a deep learning algorithm to subdivide the classification of an emotion dictionary, which can be classified by adding negative word lists, polarity conversion dictionaries, and online dictionaries, among other things, based on the theory of ecological instantaneous evaluation. It can be used for both multiclass and detailed emotion analysis. The model is more accurate in assessing the mental health of college students, according to the results of the study. The current emotional state of users can be identified, as well as signs of psychological risk, using emotional analysis of Weibo data, which will become a valuable resource for users seeking clinical psychological consultation in the future.

AI-Assisted Emotion Recognition: Impacts on Mental Health Education and Learning Motivation

“…Traditional mental health education has traditionally relied on the intuition and experience of educators, often neglecting or misinterpreting subtle emotional cues [24,25]. Concurrently, although existing AI solutions excel in certain applications, the task of deciphering and integrating multimodal data within a complex educational framework remains a significant research challenge.…”