There are many similar vehicles in city monitoring, which brings great challenges to vehicle re-identification. Local features such as front, window and roof are the subtle differences of similar vehicles. According to the attention characteristics of the thermal map of the vehicle detection algorithm, a MCRF-SSD algorithm is proposed to detect the local feature area of the vehicle, and combines it with GMM-EM clustering algorithm. The detection performance is better than the current mainstream algorithm on the open data set.At the same time, in order to increase the inter-instance and reduce the intra-instance, the Arcface loss function is introduced into the feature extraction stage. In order to improve the performance of vehicle re recognition, in the stage of global feature and local feature fusion, a focus fusion structure (FFS) method is proposed, which can preserve the spatial distribution of feature graph, and a learnable parameter is introduced to improve the efficiency of feature fusion. Experimental results show that the performance of the proposed algorithm is better than that of the current best performance scheme in public VehicleID and VeRi datasets.