The strategy of pedestrian representation merging global features with local features is frequently utilized to improve the discriminability of the model for person re-identification (re-ID) in complex scenes. However, extracting local features generally necessitates specialized models for specific semantic regions, which increases the complexity of the algorithm. Therefore, a re-ID model is proposed based on multi-scale feature learning. The model acquires discrimination information of multi-level complementary with the combination of different fine-grained local features and global features, and then to realize end-to-end person re-identification. In order to obtain high-resolution information while retaining more detailed information, max pooling and average pooling are employed to downsample the features. In addition, this paper introduces TriHard loss to constrain global features and uses random erasure to enhance the data, which further ameliorates the adaptability of the model in complex scenes. Comparative experiments on Market-1501 and DukeMTMC-reID datasets show that the accuracy of rank-1 has reached 94.9% and 87.1%, respectively, which verifies the effectiveness of the proposed method.