Person re-identification is an important task in video surveillance fields. Large variations in pose, illumination and occlusion could change the appearance of the person, which make person re-identification still be a challenging problem. Developing robust feature descriptors benefit the person matching. In this paper, we propose a new multi-feature fusion person re-identification method focusing on combining hand-crafted feature and deep feature. Specifically, we first extract hand-crafted features both on local regions and global region from each image, which can collaborate local similarities as well as global similarity to overcome the problems caused by local occlusion. Then we train CNN model which has fused three datasets to get deep feature. Finally, we present to optimize and integrate the re-identifying result of hand-crafted feature and deep feature by selective weighting combination. The results carried out on three person re-identification benchmarks including VIPeR, CUHK01 and CUHK03, which show that our method significantly outperforms state-of-the-art methods.