Retinal vascular image segmentation is an important and difficult task in medical image analysis,and it is difficult for conventional methods to detect small and dense vascular structures effectively.To solve this problem,a novel high-precision and high-accuracy retinal vascular segmentation method that combines swin transformer block (STB) and full-scale attention skip connection technology (FSASC) is proposed.By constructing a U-shaped encoder-decoder network,the proposed method realizes self-attention from local to global,so that the proposed model can pay more attention to key vascular features.FSASC structure is used for fusing different features,which provides a simple and powerful mechanism for the proposed model to learn multi-scale semantic and spatial information.The proposed method is tested by using open datasets DRIVE and STARE.The experimental results show that the method can achieve high-quality and high-precision segmentation for retinal vascular structures.Compared with Unet and other methods,the proposed method has better performance in both the detailed feature segmentation and the segmentation accuracy.