Existing person re-identification (Re-ID) methods often struggle with inaccurate feature extraction and misidentification of person features due to environmental noise.Here,we propose a multi-feature fusion branch network for person Re-ID based on dynamic convolution and attention mechanism.First,considering the uncertainties in illumination,human posture and occlusion,dynamic convolution is proposed to replace static convolution in ResNet50 to obtain a more robust Dy-ResNet50 model.Second,given the great difference in camera perspective and the likelihood of people being occluded by objects,self-attention and cross-attention mechanisms are embedded into the backbone network.Finally,the cross entropy loss function and the hard triplet loss function are used as the model's loss functions,and experiments are carried out on public datasets of DukeMTMC-ReID,Market-1501 and MSMT17.The results show that the proposed model outperforms current mainstream models in Rank-1 (first hit) and mAP (mean Average Precision) on three public datasets,indicating its high identification accuracy.