Existing methods of person reidentification based on convolutional neural network lack discriminative information, due to occlusion and complex backgrounds. To solve these problems, a method based on multi-scale convolutional feature fusion is proposed herein. In the training phase, pyramid pooling is used to extract multiple eigenvectors containing global features and multi-scale local features for blocking and pooling of the convolutional feature map. Afterward, each feature vector is classified independently, and the weights and features on the last inner layer of each class are normalized to improve the classification performance. Finally, a gradient descent algorithm is applied to optimize the sum of losses for each classification. In the recognition phase, pooled multiple feature vectors are concatenated into a new vector for similarity matching. The efficiency of the proposed algorithm is verified on datasets Market-1501 and DukeMTMC-reID, in which the results indicate that features obtained by the proposed model are more discriminative and that the Rank-1 accuracy and average accuracy are both better than most state-of-the-art algorithms.