Abstract: Aiming at the problem of trajectory evolution and single-type of detection result in trajectory anomaly detection technology,an anomaly detection method was proposed for mobile terminal user based on location information,which comprehensively utilizes the user historical behavior pattern,group structure information,and behavior of close users.The method converts the location data into the spatio-temporal co-occurrence area(STCOA),and further excavates the user behavior pattern and extractes the user group structure information.On this basis,a multi-class anomaly detection model was constructed by random forest method according to five abnormal characteristics of historical beha-vior pattern anomaly,accompanying behavior pattern anomaly,STCOA behavior pattern anomaly,STCOA flow pattern anomaly and group attribute of abnormal users.This model can identify individual anomaly,group anomaly,spatio-temporal anomaly and event anomaly of mobile terminal users.Experiments on real data sets show that the proposed me-thod can effectively identify the trajectory evolution behavior and detect various types of anomalies of mobile terminal users.Compared with the similar methods,this method has higher recall rate and lower error rate.

Key words: Abnormal classification, Abnormal feature, Location data, Mobile terminal, Trajectory evolution