In recent years, with the rapid development of the e-commerce industry, how to quickly and accurately find the required goods through image information in the huge product library has important application value.Aiming at the characteristics such as the large scale of product image data, the large difference of data between classes, the large difference between the scales of photographed products and the loss of detailed information in compressed images, an Spatial pyramid pooling -Hash-Net (SHN)model combining spatial pyramid pooling strategy and hash learning is proposed as the feature extraction part of the product image retrieval method. In order to improve the robustness of the model to image deformation, the spatial pyramid pooling strategy is adopted to achieve multi-scale feature fusion. In order to make the learned hash code have better independence, the quantization error loss and additional weights are used to constrain the hash code.The method preserves the original image information and solves the negative effects caused by image scale changes, and it can realize fast product image retrieval through hash coding.The experimental results show that the mAP value of this method reaches 91.986 3%, and the time for completing a search is 0.034 856 s. The image retrieval performance is better than the current mainstream methods.