Given a query image from a camera, person re-identification (Re-ID) can retrieve the images of the same identity from a gallery, the images of which are captured by the other cameras. Therefore, person Re-ID has been widely used in the field of video surveillance. However, person Re-ID still suffers from a series of challenges, such as illumination changes, pose variations, and occlusions. Although the person Re-ID methods based on attention mechanism give an effective and feasible solution for the above challenges, attention mechanism may make a network focus too much on the most salient discriminative features and ignore other potential discriminative features. To solve this problem, we propose a two-level salient feature complementary network (TSFC-Net) to extract the most salient discriminative features and the secondary salient discriminative features of pedestrian images for person Re-ID. Specifically, TSFC-Net first extracts the most salient discriminative features of pedestrian images by embedding the spatial and channel attention modules in the backbone network, and then extracts the secondary salient discriminative features of pedestrian images by a secondary salient feature mining module (SSFM). Since the final features of pedestrian images fuse the most salient discriminative features and the secondary salient discriminative features, TSFC-Net can significantly improve the richness and discrimination capability of pedestrian representations. In addition, we conduct extensive experiments on the Market-1501, DukeMTMC-reID, and CUHK03 data sets, and the experimental results indicate that our TSFC-Net has a better performance compared with most of the state-of-the-art person Re-ID methods.