In order to solve the problems in retinal vessel segmentation, such as blurred main vessel profile, broken micro-vessels, and missegmented optic disc boundary, a ghost convolution adaptive retinal vessel segmentation algorithm is proposed. The first algorithm uses ghost convolution to replace the common convolution in neural network, and the ghost convolution generates rich vascular feature maps to make the target feature extraction fully carried out. Secondly, the generated feature images are adaptive fusion and input to the decoding layer for classification. Adaptive fusion can capture image information at multiple scales and save details with high quality. Thirdly, in the process of accurately locating vascular pixels and solving image texture loss, a dual-pathway attention guiding structure is constructed to effectively combine the feature map at the bottom and the feature map at the top of the network to improve the accuracy of vascular segmentation. At the same time, Cross-Dice Loss function was introduced to suppress the problem of uneven positive and negative samples and reduce the segmentation error caused by the small proportion of vascular pixels. Experiments were conducted on DRIVE and STARE datasets. The accuracy was 96.56% and 97.32%, the sensitivity was 84.52% and 83.12%, and the specificity was 98.25% and 98.96%, respectively, which proves the good segmentation effect.