Identifying electricity theft user with correlation between electricity usage of user and line loss of associated feeder could facilitate electricity theft detection with low false positive rate. However, most existing approaches have stringent requirement on stability of time series of load data, which hinder engineering application of these approaches. A high-loss line theft user identification method based on segmented dynamic time bending distance is proposed. Firstly, the heuristic segmentation algorithm is used to transform the data of each user′s power consumption sequence and line loss power consumption sequence to achieve feature extraction and data reduction. Secondly, dynamic time bending distance is employed to find out the user′s power consumption most similar to the line loss power consumption pattern, and the linkage between them is analyzed. Finally, the corresponding user corresponding to the power consumption in the most similar pattern of line loss and the same fluctuation direction is designated as the suspected user of electricity theft. Based on the actual data of high-loss lines, the simulation results show that the proposed method has better accuracy and lower false positive rate than the comparison method.