The purpose of vehicle re-identification（vehicle reID）is to identify the same vehicle through different cameras. However， vehicle reID is a very challenging task due to the large intra-class difference and the large inter-class similarity of vehicle images. This paper proposes a vehicle reID method based on double-branch network feature fusion to solve this problem. The method uses two branches and batch drop block strategies to extract and fuse global features and local features for highlighting the intra-class similarities and inter-class differences. At the same time， the method uses circle loss terms instead of the traditional triplet loss terms and cross-entropy loss terms to construct the objective function. Extensive experiments of the method are conducted on the two public datasets VeRi-776 and VehicleID， and results show that its search accuracy is improved by about 5% compared with the existing methods， which verifies the effectiveness of the method.