Due to the limitation of the size of the receptive field, the traditional convolutional neural network (CNN) cannot directly and effectively obtain the key information such as spatial structure and global semantics,which leads to the difficulty in feature extraction of wide blood vessel boundary and capillary region,resulting in poor performance of retinal vascular segmentation.Therefore,a retinal vascular segmentation refinement framework based on graph convolution is proposed in this paper.In the framework,through contour extraction and uncertainty analysis method,the potential false segmentation regions in the coarse segmentation results of CNN are chosen,and the appropriate graph data is constructed by combining the extracted feature information.The graph data is sent to the residual graph convolutional network (Res-GCN) for secondary classification,and the retinal vascular refinement segmentation results are obtained.The framework can be used as a plug-and-play module to access the end of any retinal vascular segmentation network,which has the characteristics of high portability and usability.In the experiment,U-neural network (U-Net) and its representatively improved networks DenseU-Net and AttU-Net are selected as the benchmark networks,and tested on the DRIVE, STARE and CHASEDB1 datasets.The Sp of the framework is 98.28%,99.10% and 99.04%,respectively,and the Pr is 87.97%, 88.87% and 90.25%,respectively, which prove that it has the refinement ability to improve the segmentation effect of the benchmark network.