To address issues of insufficient receptive fields and weak connections between global and local features in unsupervised domain adaptive person re-identification, a multi-scale feature interaction method was proposed. Firstly, the feature squeeze attention mechanism compressed image features, which were then fed into the network to enhance rich local information representation. Secondly, the residual feature interaction module encoded global information into the features by interaction, while increasing the model's receptive field and enhancing its ability to extract pedestrian features. Finally, a bottleneck module based on partial convolution conducted convolution operations on the part of the input channels, reducing redundant computations and improving spatial feature extraction efficiency. Experimental results on three adaptation datasets demonstrate that the method mAP reached 82.9%, 68.7%, and 26.6%, the Rank-1 reached 93.7%, 82.7%, and 54.7%, the Rank-5 reached 97.4%, 89.9%, and 67.5%, by comparison with baseline, respectively, demonstrating that the proposed method allows for better pedestrian features representation and improved recognition accuracy.