In the fusion research of Synthetic Aperture Radar （SAR） and visible images， the existing methods usually face the challenges of large modal differences， information loss and high computational complexity. Therefore， an SAR and visible image fusion algorithm based on residual Swin Transformer module was proposed. Firstly， Swin Transformer was used as the backbone to extract global features， and a full-attention feature coding backbone network was used to model remote dependencies. Secondly， in order to improve fusion effect， three different fusion strategies were designed： feature fusion strategy based on L1 norm of sequence matrix， fusion strategy based on image pyramid， and additive fusion strategy. Thirdly， the final fusion result was obtained by weighted averaging the three results， which adjusted pixel value and reduced noise of SAR image effectively， better retained clear details and structural information of visible image， and fused surface feature information of SAR image and visible image at different scales. Finally， many experiments were carried out on SEN1-2 dataset， QXS-SAROPT dataset， and OSdataset. Experimental results show that compared with the algorithms such as general image fusion framework based on convolutional neural network IFCNN， and Multi-level Decomposition based on Latent Low-Rank Representation （MDLatLRR）， the proposed algorithm has better subjective visual effects with significant improvement in most objective evaluation indicators， and has excellent noise suppression and image fidelity capabilities while retaining source image features.