In this study, we propose an improved U-Net retinal vascular image segmentation algorithm by introducing some modules, such as inception, hole convolution, and attention mechanism, into the U-Net network to solve the problem of low segmentation accuracy caused by the small blood vessels in the retinal image. Initially, the inception module was added during the encoding stage, and convolution kernels of different scales were used to extract the image features to obtain multiscale information from the image. Subsequently, a cascaded hole convolution module was added to the bottom of the U-Net network for expanding the receptive field of the convolution operation without increasing the network parameters. Finally, an attention mechanism was designed for the deconvolution operation during the decoding phase. The problem of weight dispersion can be solved by focusing on the target features under the combination of the attention mechanism and jump connection mode. The experimental results obtained using the standard image set DRIVE denote that the average accuracy, sensitivity, and specificity of the proposed algorithm are 1.15%, 6.15%, and 0.67% higher than those of the traditional U-Net algorithm, respectively, and that the proposed algorithm outperforms other traditional segmentation algorithms.