Person re-identification aims to match pedestrian images across multiple surveillance camera views. It is still a challenging taskdue to the partial occlusion of pedestrian images, variations in the illumination of surveillance cameras, and similar appearances ofpedestrians and so on. In order to improve the representation ability of pedestrian features extracted from the convolutional neural networks,in this paper, we proposed an improved split-attention architecture for person re-identification. Specifically, we first divide the feature mapinto two sub-groups and then split the features in each subgroup into three more fine-grained sub-feature maps. Moreover, in order tominimize the inter-class similarity and maximize the intra-class similarity, we use circle loss and identification loss to optimize our networktogether. Circle loss makes the similarity scores learn at different paces, which benefits deep feature learning. The circle loss not onlymakes the model have higher optimization flexibility but also makes the convergence target of the model more definite. Unlike manymethods that use complex convolutional neural networks to represent pedestrian feature maps in a layer-wise manner, our proposed methodimproves the representation ability of pedestrian features at a more fine-grained level. We evaluated the performance of our proposednetwork on two large-scale person re-identification benchmark datasets Market-1501 and DukeMTMC-reID. Experimental results showthat the proposed split-attention network outperforms the state-of-the-art methods on both datasets with only using pedestrian globalfeatures.