To solve the problem of poor segmentation effect caused by small blood vessels in retinal images,loss of detailed features, gradient descent and explosion,a U-Net retinal vascular image segmentation model with residual block,cyclic convolution module and spatial channel extrusion excitation module is proposed.First,the training set is expanded by using a series of random enhancements,and then residual blocks are introduced into the U-Net model to avoid the segmentation accuracy reaching saturation and then rapidly degrading as the network depth increases.The bottom of the U-Net is replaced with a circular convolution module,the low-level features of the image are extracted,and features are continuously accumulated,the semantic information between contexts is enhanced,and a more effective segmentation model is obtained.Finally,the concurrent spatial and channel squeeze and channel excitation module is embedded between the convolutional layers.The excitation module finds the channel with stronger characteristic signal, and emphasizes this channel, compresses irrelevant channels,and reduces the interference of irrelevant characteristic information.Through the verification results on the DRIVE data set,the accuracy of the model proposed in this paper is 98.42%,the sensitivity reaches 82.36%,and the specific value reaches 98.86%.Compared with other network segmentation methods,the segmentation method proposed in this article has better segmentation effect.