Person re-identification faces challenges such as posture change, occlusion interference, and illumination difference, and thus it is very important to extract pedestrian features with strong discriminability. In this paper, an improved person re-identification method based on global features is proposed. Firstly, a multi-receptive field fusion module is designed to fully obtain pedestrian context information and improve the global feature discriminability. Secondly, generalized mean (GeM) pooling is used to obtain fine-grained features. Finally, a multi-branch network is constructed, and the features of different depths of the network are fused to predict the identity of pedestrians. The mAP indexes of this method on Market1501 and DukeMTMC-ReID are 83.8% and 74.9%, respectively. The experimental results show that the proposed method can effectively improve the model based on global features and raise the recognition accuracy of person re-identification.