At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient multi-scale channel attention network (EMCA) to learn robust and more discriminative features to solve these problems. Specifically, we designed a novel cross-channel attention module (CCAM) in EMCA and placed it after different layers in the backbone. The CCAM includes local cross-channel interaction (LCI) and channel weight integration (CWI). LCI focuses on both the maximum pooling features and the average pooling features to generate channel weights through convolutional layers, respectively. CWI combines the two channel weights to generate richer and more discriminant channel weights. Experiments on four popular person Re-ID datasets (Market-1501, DukeMTMC-ReID, CUHK-03 (detected) and MSMT17) show that the performance of our EMCA is consistently significantly superior to the existing state-of-the-art methods.