The morphological structure of retinal vessels is an important index to reflect human health.In order to solve the existing problems in retinal vessel segmentation,such as blurred main vessels,broken microvessels and false segmentation of optic disc,the multi-scale feature fusion double U-shaped retinal segmentation algorithm is proposed.Firstly,the low level U-Net efficient cyclic residual module is used for coarse-grained segmentation of fundus images to obtain the initial contour of retinal vessels.Secondly,the coarse segmentation image is multiplied by the pixels of the original feature image into the high level U-Net,and the scaling wide residual model is used to decode the fine-grained image to enrich the details of retinal vessels.At the sametime,the three pathway attention mechanism is used to connect the encoding layer and decoding layer of the double network in a compound way to realize the cross network propagation of feature mapping and reduce the semantic difference of context.Finally,the double U-shaped network can extract vascular pixels at a deeper level and accurately segment retinal details.Experiments were conducted on DRIVE and STARE datasets,the accuracy was 96.45% and 97.02%,the sensitivity was 83.35% and 81.40%,and the specificity was 98.38% and 98.83%,respectively.The overall performance is better than existing algorithms.