Video-based person re-identification problems are caused by perspective changes, lighting variations, background clutter, occlusion, appearance similarity, motion similarity, and mismatch resulting from the distance difference of same person with different modal features. This study proposes a video-based person re-identification method that combines multi-level deep feature representation and ordered weighted distance fusion. During the stage of person feature representation, the multi-level deep feature representation network proposed herein not only learns the space-time features of the persons in video sequences but also acquires the persons' global and local appearance features. In the stage of the ordered weighted distance fusion, the feature representations of persons are firstly input into distance metric learning, and the independent distances of persons under three types of features are calculated. The fusion algorithm then sorts the distances to optimize distance weights according to distance ranking. Finally, to accurately match a person, the algorithm fuses the three types of distances to obtain the final distance. Experimental results compared with the results of related methods in public datasets show that the proposed method not only improves the recognition rate of video-based person re-identification but also possesses abundant and integral ability for person feature representation.