In the diagnosis of ophthalmic diseases, segmentation of retinal blood vessels is a quite effective method. However, using this method, difficulties in blood vessel segmentation are often encountered due to the low contrast of the retinal blood vessel background and complex details of the blood vessel end. Thus, residual learning is introduced by adding a basic U-net network to the process of network design. Through the introduction of residual learning and attention mechanism modules into the basic U-net network in the process of network design, a new type of U-net-based RAU-net retinal blood vessel image segmentation algorithm is proposed. First, the residual module is added to the encoder stage of the network to address gradient explosion and disappearance caused by the deepening of the model network. Second, the attention gate module is introduced in the decoder stage of the network to suppress unnecessary features to ensure high accuracy of the model. Through verification of DRIVE, the accuracy, sensitivity, specificity, and F1-score of the algorithm reached 0.7832, 0.9815, 0.9568, and 0.8192, respectively. Thus, the segmentation effect was better than that of ordinary supervised learning algorithms.