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基于神经网络的当代大学生心理健康预测与分析。

Prediction and Analysis of Contemporary College Students' Mental Health Based on Neural Network.

机构信息

School of Education, Xi'an Fanyi University, Xi'an, Shannxi 710105, China.

出版信息

Comput Intell Neurosci. 2022 Jul 13;2022:7284197. doi: 10.1155/2022/7284197. eCollection 2022.

DOI:10.1155/2022/7284197
PMID:35875757
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9300349/
Abstract

For an ordinary student who has just graduated from high school, interpersonal communication and performance evaluation on campus is also a huge challenge. In order to solve the future and current competition and pressure faced by contemporary college students, many college students have mental health problems. This paper evaluates, predicts, and analyzes the mental health status of contemporary college students based on a neural network algorithm. The computer technology of neural network algorithm is applied to the prediction of contemporary college students' mental health. Data mining technology based on a neural network algorithm is used to collect data sources. Finally, the prediction results are analyzed, and the main psychological stressor factors of contemporary college students are analyzed by cluster analysis. The results show that there is no significant correlation between college Students' inferiority complex and dependency map and the incidence of mental diseases and majors. A comprehensive physical symptom test was conducted on individuals to understand students' psychological characteristics and behavior.

摘要

对于刚高中毕业的普通学生来说，校园中的人际交往和表现评价也是一个巨大的挑战。为了解决当代大学生未来和当前所面临的竞争和压力，许多大学生都存在心理健康问题。本文基于神经网络算法评估、预测和分析当代大学生的心理健康状况。将神经网络算法的计算机技术应用于当代大学生心理健康的预测。基于神经网络算法的数据挖掘技术用于收集数据源。最后，通过聚类分析对预测结果进行分析，分析当代大学生的主要心理压力源因素。结果表明，大学生自卑感和依存图与精神疾病的发病率和专业之间没有显著相关性。对个体进行了全面的身体症状测试，以了解学生的心理特征和行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/92610ed14b2c/CIN2022-7284197.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/860f8ddc0b4a/CIN2022-7284197.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/c4f76f170f08/CIN2022-7284197.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/efbc95d4eef7/CIN2022-7284197.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/8fefc86089aa/CIN2022-7284197.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/92610ed14b2c/CIN2022-7284197.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/860f8ddc0b4a/CIN2022-7284197.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/8f0093f062a2/CIN2022-7284197.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/2d0e435c133e/CIN2022-7284197.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/5f3ce5c2de94/CIN2022-7284197.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/bf0dcd858bae/CIN2022-7284197.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/c4f76f170f08/CIN2022-7284197.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/efbc95d4eef7/CIN2022-7284197.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/8fefc86089aa/CIN2022-7284197.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d0d/9300349/92610ed14b2c/CIN2022-7284197.009.jpg

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Transl Psychiatry. 2020 Dec 14;10(1):430. doi: 10.1038/s41398-020-01117-5.
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Educ Psychol Meas. 2024 Jul 25:00131644241262964. doi: 10.1177/00131644241262964.