To solve the few-shot problem caused by insufficient available pedestrian images in person re-identification, based on the Bi-Similarity, a few-shot person re-identification method based on multi-scale mixed attention and metric fusion is proposed. In this work, firstly, a multi-scale mixed attention method is introduced into the feature embedding module. This method introduces spatial attention in different feature extraction layers and introduces channel attention in the feature fusion between different scale layers, which can extract more discriminative pedestrian features. Secondly, a dual metric method combining Euclidean and cosine distance is proposed in the metric module to comprehensively measure the absolute spatial distance and directional difference of pedestrian features. In this way, the reliability of pedestrian similarity measurement is improved. Then, pedestrian feature similarity scores are obtained separately using the dual metric and relation metric methods. Finally, the combined metric score is obtained by weighted fusion, and the combined metric score is used to construct the joint loss to realize the overall optimization and training of the network. Experimental results on three small datasets, Market-mini, Duke-mini, and MSMT17-mini, show that the proposed method significantly improves recognition performance compared to other few-shot learning algorithms. Specifically, in scenarios 5-way1-shot and 5-way5-shot, the average recognition accuracies are 90.40% and 95.69%, 86.77% and 94.96%, and 71.08% and 82.63%, respectively.