In order to solve the problem that the label is not accurate and the background interference makes it difficult to obtain the predefined local area in the weak supervision vehicle re-identification method. A vehicle re-identification network based on weak supervised attention and knowledge sharing is proposed. In the weak-supervised attention module (WAM), the weak-supervised method is used to generate the vehicle component mask, and the component channel alignment step enables the module to perform adaptive feature alignment under complex background. Aiming at the problem that the mask of WAM module is unstable due to the low accuracy of labels in weak supervision method, a knowledge sharing module is constructed in local branches. The module uses migration learning to extract vehicle component features from WAM module, and performs multi-scale component feature extraction to prevent unstable vehicle component mask generation. Through experiments, mAP, CMC@ 1 and CMC@ 5 reached 82. 12%, 98. 50% and 99. 12%, respectively, which are better than the existing methods and verify the effectiveness of this method.