It has become an important technique and tactics for the public security organs to infer the type of shoes worn by the perpetrators according to the shoe prints left at the scene, and then search the suspected type of shoes in the surrounding surveillance video. This technique is completely dependent on manual screening, which is greatly affected by subjective factors and easily leads to problems such as missed detection. To solve this problem, this paper proposes a shoe type recognition algorithm based on attention mechanism. First, close to the actual combat of public security criminal investigation, a multi background monitoring shoe data set with sample size of 300 is established. Then, an attention mechanism model is proposed to enhance the ability of the residual network (ResNet50) to extract important features of shoes. Finally, the effects of selecting the output of different feature layers as shoe features and different convolution feature aggregation methods on the recognition accuracy are compared. In order to enhance the generalization ability of the model, label smoothing is added to the loss function. The experimental results on the multi background data set show that the Rank-1 and mean average precision of the algorithm are 74.32% and 56.97%, respectively.