摘要
当前高校学生心理危机状态识别方法存在错误率高、准确性差以及识别时间长等缺陷,为了改善高校学生心理危机状态识别的效果,提出基于数据挖掘技术的高校学生心理危机状态识别方法。首先分析当前高校学生心理危机状态识别研究进展,并指出当前高校学生心理危机状态识别方法存在的缺陷;然后采集高校学生心理危机状态数据,采用数据挖掘技术中的支持向量机对心理危机状态数据进行分析和建模,建立高校学生心理危机状态识别模型;最后进行高校学生心理危机状态识别的仿真实验。实验结果表明,该方法的高校学生心理危机状态识别正确率超过90%,拒识率和误识率极低,识别效果明显优于其他方法,可以为高校学生心理管理人员提供有价值的信息。
Since there are some defects in the current methods,such as high error rate,poor accuracy and long identification time,a college students′psychological crisis state recognition method based on data mining technology is proposed to improve the recognition effect.Firstly,the research progress of recognition for the current college students′psychological crisis state is analyzed,and the defects of these recognition methods are pointed out.And then,the data of college students′psychological crisis state are collected.The support vector machine(SVM)in data mining technology is used to analyze and model the collected data,so as to establish the recognition model of university students′psychological crisis state.The simulation experiment of college students′psychological crisis state recognition was carried out.The result shows that the recognition correct rate of the proposed method is above 90%,and its rejection rate and false recognition rate are extremely low.Its recognition effect is obviously better than that of the other methods,so it can provide valuable information for college students′psychological management personnel.
作者
徐长文
XU Changwen(Northeastern University,Shenyang 110819,China)
出处
《现代电子技术》
2021年第11期120-124,共5页
Modern Electronics Technique
