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基于支持向量机的大学生心理健康分析模型研究
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Abstract:
大学生处于校园与社会多元化的复杂环境，易出现心理健康问题，存在分析时费时费力且具有主观性等问题，本文提出基于支持向量机的大学生心理健康分析方法。首先，构建高质量大学生心理健康语料库；其次，选取有效特征；再次，结合支持向量机算法，构建分析模型；最后，实验结果证明，测试构建的模型，正确率达到88.5%。因此，本文提出的方法有效、科学。
College students are in the complex environment of campus and social diversity, prone to mental health problems. There are time-consuming and laborious analysis and subjective problems. This paper proposes a support vector machine based mental health analysis method for college students. Firstly, the necessary corpus is constructed. Secondly, effective features are selected. Thirdly, combined with the support vector machine algorithm, the model is constructed. Finally, the accuracy of the constructed model is up to 88.5%. Therefore, the method proposed in this paper is effective and scientific.
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