In order to solve the problem of spatial information loss caused by the splitting of vehicle feature maps in the process of vehicle re-identification， a module combining the relationship between stripe features was proposed to compensate for the lost spatial information. First， a two-branch neural network model was constructed for the special physical structure of the vehicle， and the output feature maps were divided horizontally and vertically equally and trained on different branches of the neural network. Then， a multi-activation value module was designed to reduce noise and enrich the feature map information. After that， triplet and cross-entropy loss functions were used to supervise the training of different features to restrict the intra-class distance and enlarge the inter-class distance. Finally， the Batch Normalization （BN） module was designed to eliminate the differences of different loss functions in the optimization direction， thereby accelerating the convergence of the model. Experimental results on two public datasets VeRi-776 and VehicleID show that the Rank1 value of the proposed method is better than that of the existing best method VehicleNet， which verifies the effectiveness of the proposed method.