A vehicle re-identification algorithm based on attention mechanism and adaptive loss weights was proposed in order to solve the problem that the vehicle re-identification algorithms cannot adequately represent vehicle features due to intra-class differences and inter-class similarities. The improved backbone network ResNet50_ibn was used to avoid the influence of objective factors such as color, illumination and perspective, and extract invariant features about the target. An attention mechanism was introduced to build a group representation network, which fused the interdependence between features by extracting from different groups and extracted abundant feature information from different feature representations. The loss function was improved by adaptive loss weight, and the network was trained by using a multi-loss function strategy. The algorithm achieved 96.0% Rank-1, 79.8% mAP and 81.5% Rank-1, 80.9% mAP, respectively on the public datasets VeRi776 and VehicleID. The experimental results show that the features extracted by the algorithm are more discriminative. The comprehensive performance is better than other existing vehicle re-identification algorithms.