Abstract: To solve the problems of discontinuity of image features caused by hard horizontal division during the extraction of player re-recognition features in multiplayer matches, and noise features introduced by joint points recognition errors, a joint sliding window and weighted scoring player re-recognition algorithm is proposed. Firstly, the ResNet-50 backbone network is modified and spatial relationships are used to perceive attention to obtain feature maps with less noise. Secondly, the sliding window division strategy is adopted so that each horizontal band contains part of the features of its adjacent area to maintain the continuity of image features. Then, the pose information is used to extract local joint point features, give the effective features higher weight, and score them through multiple perception layers to optimize the joint point score. Finally, the joint loss function strategy is used to constrain the learning of the network. Experiments were carried out on Occluded-Duke, Market-1501 and self-built datasets, and the results showed that the Rank-1 index of the proposed algorithm reached 60.90%, 94.70% and 80.35% on the three datasets, respectively.