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

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 significan...

Full description

Saved in:
Bibliographic Details
Published in:Scientific programming Vol. 2022; pp. 1 - 10
Main Author: Hong, Juan
Format: Journal Article
Language:English
Published: New York Hindawi 14.07.2022
John Wiley & Sons, Inc
Subjects:
Algorithms
College students
Colleges & universities
Dictionaries
Education
Emotional factors
Emotions
Health education
Machine learning
Mental disorders
Mental health
Risk analysis
Social support
Students
Teaching
ISSN:1058-9244, 1875-919X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/1695044