A feature extraction network using global and local features is designed to tackle the issue of retrieving incomplete and fuzzy shoe prints. The global features of the multiscale shoe print are normalized and weighted, and the losses of all their outputs are calculated; moreover, the part-based Conv baseline (PCB) module is used to divide the shoe print feature map into three parts, extract the local features of the three parts, and calculate their losses. During the training phase, all of the global feature branch and local feature branch losses are added to express them collectively. The output of the two branches after splicing is directly flattened as the shoe print descriptor to be retrieved in the test phase, and the cosine distance between it and the descriptor of the sample library shoe print is used as the similarity score. The experimental results show that the proposed method significantly reduces the parameter quantity and calculation cost of the model, and achieves high accuracy on the three shoe print data sets of CSS-200, CS Database, and FID-300. Furthermore, it achieves decent accuracy on the top1% of the CSS-200 and CS Database (Dust) datasets, which are 94.5% and 95.45%, respectively.