The feature extraction process in deep supervised Hash image retrieval has been dominated by the convolutional neural network architecture. However， with the application of Transformer in the field of vision， it becomes possible to replace the convolutional neural network architecture with Transformer. In order to address the limitations of existing Transformer-based hashing methods， such as the inability to generate hierarchical representations and high computational complexity， a deep supervised hash image retrieval method based on Swin Transformer is proposed. The proposed method utilizes the Swin Transformer network model， and incorporates a hash layer at the end of the network to generate hash encode for images. By introducing the concepts of locality and hierarchy into the model， the method effectively solve the above problems. Compared with 13 existing state-of-the-art methods， the method proposed in this paper has greatly improved the performance of hash retrieval. Experiments are carried out on two commonly used retrieval datasets， namely CIFAR-10 and NUS-WIDE. The experimental results show that the proposed method achieves the highest mean average precision （mAP） of 98.4% on the CIFAR-10 dataset. This represents an average increase of 7.1% compared with the TransHash method and an average increase of 0.57% compared with the VTS16-CSQ method. On the NUS-WIDE dataset， the proposed method achieves the highest mAP of 93.6%. This corresponds to an average improvement of 18.61% compared with the TransHash method， and an average increase of 8.6% in retrieval accuracy compared with the VTS16-CSQ method.