Semantic communication is a type of communication designed to convey semantic information, which is characterized by the fact that it can effectively reduce redundancy and the amount of transmitted data. Currently the research on semantic communication is only in its infancy, and more theoretical research can help to promote the real implementation of semantic communication systems. The core technology for realizing semantic communication, end-to-end joint source channel coding, has made great progress in the past few years, and semantic images have also been developed. In order to solve the problems of computational inefficiency and insufficient semantic feature extraction, a new neural network JSCC is designed in this paper.Specifically, inspired by the excellent performance of Swin Transformer in visual tasks, the Swin-Transformer module is combined with residual networks for the first time, and a Swin Transformer-based image semantic communication system. In order to solve the problems such as the poor efficiency of traditional CNN for image feature extraction, the attention residual network module is introduced to extract the image semantic features initially, and then the image semantic features are further extracted by Swin Transformer. Through the verification of the experimental results, compared with the existing schemes, the proposed scheme in this paper achieves higher than 2 dB performance improvement in PSNR and more than 5% performance improvement in MS-SSIM performance