【摘要】 利用C5.0算法构建大学生心理健康预测的决策树模型和分类规则，利用SPSS Clementine数据挖掘工具对大学生心理卫生测评数据进行分析，以此对大学生心理健康状态进行了预测。研究发现：大学生的心理健康普遍存在一定的问题，应针对不同症状采取不同的干预措施；强迫症状、精神病性、抑郁、焦虑和人际关系敏感五个属性在大学生心理健康问题中占有较大的比重。更多还原

【Abstract】 Using C5.0 algorithm to construct a decision tree model and classification rules for college students’ mental health prediction, SPSS Clementine data mining tool is used to analyze college students’ mental health evaluation data, so that the mental health status of college students is predicted. The study found that there are some common problems in college students’ mental health,and different interventions should be taken for different symptoms; The five attributes of obsessive compulsive symptoms, psychosis,depression, anxiety, and interpersonal sensitivity account for a large proportion in college students’ mental health problems.更多还原