The traditional handcrafted features rely heavily on the appearance characteristics of pedestrians and the deep convolution feature is a high-dimensional feature, so, it will consume a lot of time and memory when the feature is directly used to match the image. Moreover, features from higher levels are easily affected by human pose or background clutter. Aiming at these problems, a method based on deep multi-view feature distance learning was proposed. Firstly, a new feature to improve and integrate the convolution feature of the deep region was proposed. The convolution feature was processed by the sliding frame technique, and the integration feature of low-dimensional deep region with the dimension equal to the number of convolution layer channels was obtained. Secondly, from the perspectives of the deep regional integration feature and the handcrafted feature, a multi-view feature distance learning algorithm was proposed by utilizing the cross-view quadratic discriminant analysis method. Finally, the weighted fusion strategy was used to accomplish the collaboration between handcrafted features and deep convolution features. Experimental results show that the Rank1 value of the proposed method reaches 80.17% and 75.32% respectively on the Market-1501 and VIPeR datasets; under the new classification rules of CHUK03 dataset, the Rank1 value of the proposed method reaches 33.5%. The results show that the accuracy of pedestrian re-identification after distance-weighted fusion is significantly higher than that of the separate feature distance metric, and the effectiveness of the proposed deep region features and algorithm model are proved.