Person reidentification (ReID) is an important issue in the field of image processing and computer vision. Because pedestrian images are often affected by various interference factors, such as occlusion, illumination changes, posture changes, and background changes, extracting discriminative features is an important method to improve the accuracy of ReID. Based on the two existing methods of pose-sensitive embedding and batch feature erasing, a new feature extraction model for person ReID tasks is proposed. The model uses the view information as global features and uses the batch feature erasure method to extract fine-grained features. The mutual complementarity of the two features improves the accuracy of person ReID. In addition, by introducing the attention module, the structure of the complex network becomes concise and the amount of calculation becomes smaller. Through a large number of experiments on three public datasets, it can be seen that the proposed model can effectively deal with the occlusion environment, and it can also obtain competitive results when compared with other state-of-the-art models.