基于大数据的高校学生心理危机智能预警模型构建
基于大数据的高校学生心理危机智能预警模型构建
基金项目:
四川省科技厅重点研发项目（No：2022YFG0187）
摘要:
目的 本研究基于大学生心理健康测评数据，综合学生基本信息及日常行为数据，利用大数据、人 工智能技术，探索建立大学生心理危机智能预警模型。方法 采用整群抽样法，选取某高校部分在校大学生作为 测试样本；采取大数据技术，分析提取影响大学生心理健康问题的特征量；采取神经网络技术，构建大学生心理危 机预警模型。结果 1）成功提取大学生心理健康问题的有效特征量，其中心理健康症状特征因子 4 个，分别是强 迫、人际关系敏感、抑郁等 3 个单症状因子，1 个多症状因子；学生基本信息特征量 4 个，分别是母亲教养方式、父 亲教养方式、家庭经济条件、有无心理治疗（咨询）史；学生日常行为特征量 2 个，分别是学生学业情况和出勤情 况。2）实现特征量归一化处理，通过数据对比分析及特征量的影响大小，分别对 3 个方面的 10 个特征量赋予权重 并进行归一化处理。3）完成大学生心理危机预警模型构建。结论 本研究提取了影响大学生心理健康问题的主 要特征，建立了大学生心理危机预警指标体系，并结合心理危机预警等级，利用神经网络技术，搭建了大学生心理 危机预警模型，为及时有效干预大学生心理危机提供了新的解决方案。
Abstract:
Objective This study explores the construction of an intelligent early warning model for college students＇psychological crisis based on college students＇mental health assessment data，synthesizing students＇basic information and daily behavioral data，and utilizing big data and artificial intelligence technology. Methods The cluster sampling method was used to select some college students in a university as test samples. Big data technology was adopted to analyze and extract the characteristic quantities affecting college students＇mental health problems. And neural network technology was adopted to construct an early warning model for psychological crisis of college students. Results 1. The valid characteristic quantities of mental health problems were extracted，including 4 characteristic factors of students＇mental health symptoms，which were 3 single-symptom factors such as obsessive-compulsive symptoms， interpersonal sensitivity and depression，and 1 multi-symptom factor; Four characteristic quantities of students＇basic information，which were mother＇s parenting style，father＇s parenting style，family economic conditions，and history of psychotherapy （counseling）；Two characteristic quantities of students＇daily behavior，which were students＇academic performance and attendance. 2. The characteristic quantities were normalized. Ten characteristic quantities in 3 aspects were weighted and normalized based on the comparative analysis of data and the influence of each characteristic quantity. 3. The construction of an early warning model for psychological crisis of college students was completed. Conclusion An early warning indicator system of college students＇psychological crisis is established by extracting characteristic quantities of college students＇mental health problems. And combined with the early warning level of psychological crisis，an early warning model for college students＇psychological crisis is constructed by using neural network technology，which provides a new solution for the timely and effective intervention of psychological crisis.
引用本文王计生;徐多勇;唐莉;熊梅;江永燕;.基于大数据的高校学生心理危机智能预警模型构建[J].成都医学院学报,2024,19(1):111-115
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