基于数据挖掘的大学生心理健康与就业关系研究

Bin Liu
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引用次数: 0

摘要

为了更准确地把握大学生的就业心理,解决大学生的内心焦虑,关联规则Apripri算法构建了基于数据挖掘的大学生心理健康与就业的相关分析模型。关联规则对网络故障的诊断准确率为98.47%,诊断时间为0.21s。在不同模型的性能对比实验中,均值均在0.8以上,精密度为0.86,精密度为0.84,召回率为0.84,F1值为0.87。结果表明,本文所采用的方法符合研究要求。在不同算法性能指标的对比实验中,均值的准确率为0.87,精密度为0.85,召回率为0.84,F1值为0.88。本文的研究方法符合研究要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Relationship Between College Students' Mental Health and Employment Based on Data Mining
In order to grasp the employment psychology of college students more accurately and solve their inner anxiety, the Apripri algorithm of association rules constructs the correlation analysis model of college students' mental health and employment based on data mining. The diagnosis accuracy of association rules for network fault is 98.47%, and the diagnosis time is 0.21s. In the performance comparison experiments of different models, the mean value is above 0.8, the precision is 0.86, the precision is 0.84, the recall is 0.84, and the F1 value is 0.87. It shows that the means of this paper meet the research requirements. In the comparative experiments of different algorithm performance indicators, the accuracy of the mean is 0.87, the precision is 0.85, the recall is 0.84, and the F1 value is 0.88. The means of this paper meet the research requirements.
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