Xinxiang University, Xinxiang, Henan 453003, China.
J Healthc Eng. 2021 Oct 28;2021:3361755. doi: 10.1155/2021/3361755. eCollection 2021.
Mental health issues are alarmingly on the rise among undergraduates, which have gradually become the focus of social attention. With the emergence of some abnormal events such as more and more undergraduates' suspension, and even suicide due to mental health issues, the social attention to undergraduates' mental health has reached a climax. According to the questionnaire of undergraduates' mental health issues, this paper uses keyword extraction to analyze the management and plan of undergraduates' mental health. Based on the classical TextRank algorithm, this paper proposes an improved TextRank algorithm based on upper approximation rough data-deduction. The experimental results show that the accurate rate, recall rate, and 1 of proposed algorithm have been significantly improved, and the experimental results also demonstrate that the proposed algorithm has good performance in running time and physical memory occupation.
心理健康问题在大学生中惊人地呈上升趋势,逐渐成为社会关注的焦点。随着一些异常事件的出现,如越来越多的大学生因心理健康问题而被停学,甚至自杀,社会对大学生心理健康的关注达到了高潮。本文通过对大学生心理健康问题的问卷调查,采用关键词提取的方法对大学生心理健康的管理与规划进行分析。基于经典的 TextRank 算法,本文提出了一种基于上近似粗糙数据约简的改进 TextRank 算法。实验结果表明,所提算法的准确率、召回率和 F1 值均有显著提高,实验结果也表明,所提算法在运行时间和物理内存占用方面具有良好的性能。