Owing to the continuous changes in vehicles under different road monitoring perspectives, vehicle re-identification is still a challenging task in intelligent traffic system. Most of the existing vehicle re-identification methods are based on the appearance attributes of the vehicle, but the recognition is affected by factors such as illumination and angle, which leads to poor recognition results. Therefore, this paper designs a vehicle posture perception attention enhancement network to improve the re-identification effect of vehicles under the influence of factors such as illumination and angle. First, input the image to the convolutional pose machine to generate 12 keypoints to reconstruct the vehicle frame, and then compare the input image vehicle with the target image vehicle to extract the features of the intersecting area between two images; Finally, the global distance and local loss of vehicle features are calculated, and the recognition results are sorted according to the final results. This paper verifies on vehicle ID and VeRi776 data sets. The experimental results prove that the Top10 detection accuracy of the proposed network is increased by about 10% than other models.