Aiming at the problem of low segmentation accuracy due to small blood vessels and retinopathy in fundus blood vessel images, a U-Net retinal blood vessel image segmentation model that introduces residual blocks, cascaded cavity convolution, and embedded attention mechanism is proposed. First, increase the resolution of the retinal image, crop the data set with point noise as the center and 512 as the side length, and then introduce residual blocks in the U-Net model to increase the utilization of pixel features and avoid the degradation of deep networks; And replace the bottom of the U-Net network with a cascaded hole convolution module to expand the receptive field of the feature map and extract richer pixel features; finally, the attention mechanism is embedded in the decoder to increase the weight of the target feature and slow down useless information Interference. The experimental results based on the CHASE data set show that the accuracy of the proposed model reaches 98. 2%, the sensitivity reaches 81. 72%, and the singular value reaches 98. 90%. Compared with other multi-scale neural network methods, it embodies better segmentation results, and fully verifies that the improved U-Net network model can effectively improve the accuracy of blood vessel segmentation and assist in the diagnosis of vascular disease.