Concerning the problem of inaccurate identification of taxi shift behaviors， an accurate identification method of taxi shift behaviors based on trajectory data mining was proposed. Firstly， after analyzing the characteristics of taxi parking state data， a method for detecting taxi parking points in non-operating state was proposed. Secondly， by clustering the parking points， the potential taxi shift locations were obtained. Finally， based on the judgment indices of taxi shift event and the kernel density estimation of the taxi shift time， the locations and times of the taxi shift were identified effectively. Taking the trajectory data of 4 416 taxis in Fuzhou as the experimental samples， a total of 5 639 taxi shift locations were identified. These taxi shift locations are in the main working areas of citizens， transportation hubs， business districts and scenic spots. And the identified taxi shift time is mainly from 4：00 to 6：00 in the morning and from 16：00 to 18：00 in the evening， which is consistent with the travel patterns of Fuzhou citizens. Experimental results show that， the proposed method can effectively detect the time-space distribution of taxi shift， and provide reasonable suggestions for the planning and management of urban traffic resources. The proposed method can also help the people to take a taxi more conveniently， improve the operating efficiency of taxis， and provide references for the site selection optimization of urban gas stations， charging stations and other car related facilities.