Overview: The state of retinal blood vessels is an important indicator for clinicians in the auxiliary diagnosis of eye diseases and systemic diseases. In particular, the degree of atrophy and pathological conditions of retinal blood vessels are the key indicators for judging the severity of the diseases. Automatic segmentation of retinal blood vessels is an indispensable step to obtain the key information. Good segmentation results are conducive to accurate diagnosis of the eye diseases. Due to the good characteristic of U-Net that can use skip connection to connect multi-scale feature maps, it performs well in segmentation tasks with small data volume, therefore, it could be applied to retinal vascular segmentation. However, U-Net ignores the features of retinal blood vessels in the training process, resulting in the inability to fully extract the feature information of blood vessels, while its segmentation results show that the vessel pixels are missing or the background noise is incorrectly segmented into blood vessels. Researchers have made various improvements on U-Net for the retinal vessel segmentation task, but the methods still ignore the global structure information and boundary information of retinal vessels. To solve the above problems, a boundary attention assisted dynamic graph convolution retinal vessel segmentation model based on U-Net is proposed in this paper, which supplements the model with more sufficient global structure information and blood vessel boundary information, and extracts more blood vessel feature information as much as possible. First, RGB image graying, contrast-limited adaptive histogram equalization, and gamma correction were used to preprocess the retinal images, which can improve the contrast between the vascular pixels and background, and even improve the brightness of some vascular areas. Then, rotation and slice were adopted to enhance the data. The processed images were input into the model to obtain the segmentation result. In the model, dynamic graph convolution was embedded into the decoder of U-Net to form multiscale structures to fuse the structural information of feature maps with different scales. The method not only can enhance the ability of dynamic graph convolution to obtain global structural information but also can reduce the interference degree of the noise and the segmenting incorrectly background on the vascular pixels. At the same time, in order to strengthen the diluted vascular boundary information in the process of up-down sampling, the boundary attention network was utilized to enhance the model’s attention to the boundary information for the sake of improving the segmentation performance. The presented model was tested on the retinal image datasets, DRIVE, CHASEDB1, and STARE. The experimental results show that the AUC of the algorithm on DRIVE, CHASEDB1 and STARE are 0.9851, 0.9856 and 0.9834, respectively. It is proved that the model is effective.Aiming at the problem of missing and disconnected capillary segmentation in the retinal vascular segmentation task, from the perspective of maximizing the use of retinal vascular feature information, by adding the global structure information and retinal blood vessels boundary information, based on the U-shaped network, a dynamic graph convolution for retinal vascular segmentation model assisted by boundary attention is proposed. The dynamic graph convolution is first embedded into the U-shaped network to form a multi-scale structure, which improves the ability of the model to obtain the global structural information, and thus improving the segmentation quality. Then, the boundary attention network is utilized to assist the model to increase the attention to the boundary information, and further improve the segmentation performance. The proposed algorithm is tested on three retinal image datasets, DRIVE, CHASEDB1, and STARE, and good segmentation results are obtained. The experimental results show that the model can better distinguish the noise and capillary, and segment retinal blood vessels with more complete structure, which has generalization and robustness.