Image compressive sensing is a technology that reconstructs the original image as far as possible under the condition of under-sampling.Most of the image compressive sensing methods based on the framework of Convolutional Neural Network (CNN) are prone to be limited by the receptive field of convolution and pay less attention to global information.To solve the problem, an image reconstruction network using compressive sensing based on Swin Transformer is proposed.The network uses the convolutional layer for image sampling, and then uses the structure of Residual Swin Transformer Group (RSTG), which combines the self-attention mechanism with the residual structure, to focus on the details of the image.The experimental results show that the image reconstruction network using compressive sensing based on Swin Transformer can make full use of the prior information of the image, further improve the image reconstruction accuracy of compressive sensing, and obtain better reconstruction performance and visual effects than that of other compressive sensing methods.