大学生异常行为检测是高校学生管理中一个值得研究的课题。大学生行为检测的研究有助于高校科学快速地评估异常学生人群。针对高校传统的问卷调查、定期排查等滞后的管理手段，提出一种基于特征加权的密度峰值聚类的大学生异常行为检测算法。该算法首先采用加权特征的欧式距离来表示样本距离，然后通过密度峰值聚类对学生样本进行无标签归类，最后将局部密度不超过边界区密度的样本点分辨为异常点。

The detection of abnormal behavior of college students is a worthy research topic in college student management, whichhelps universities to evaluate abnormal student populations scientifically and quickly. Aiming at the lagging management methodssuch as traditional questionnaire survey and regular screening in universities, this paper proposes an algorithm to detect the abnormalbehavior of college students based on feature-weighted density peak clustering. The algorithm first adopts the Euclidean distance ofthe weighted feature to represent the sample distance, then uses the density peak clustering to classify the student samples without la-bel, and finally distinguishes the sample points whose local density does not exceed the boundary density as abnormal points.