The change of vehicle view may affect the accuracy of the re-identification algorithm. To solve the influence of changing viewpoints on the accuracy of re-identification, we propose a vehicle re-identification method based on local features and viewpoint perception. First, a parsing module is trained to parse a vehicle into four different views, front, back, side, and top. In this way, the fine-grained representation of the vehicle is improved. Then, we intrduce a vehicle viewpoint-aware network. The output of the network is the predicted probability information of the viewpoint, and the vehicle viewpoint perception effect is dynamically and smoothly represented according to the probability information. Finally, the viewpoint-aware effect is used to assign different weights to each local area of the vehicle to shorten the intra-class distance, expand the inter-class distance, and reduce the impact of viewpoint changes on vehicle re-identification. This method is evaluated on public datasets, including VeRi776 and VehicleID. The accuracy of mAP on VeRi776 dataset has achieved 80. 9% . Experimental results show that the proposed method can effectively improve the accuracy of vehicle re-identification. Ablation experiments demonstrate the effectiveness of the viewpoint-aware smooth representation for vehicle reidentification from multiple viewpoints.