The segmentation accuracy of retinal vessels has an important impact on the early diagnosis of ophthalmic diseases and diabetes.Facing the problem of poor segmentation performance of existing methods in microvascular and lesion regions,a segmentation model with enhanced extraction of vessel features is proposed in this paper.The model introduces a multi-scale feature extraction residual module (MFE-residual) and a multi-level residual null convolution layer at the encoding site,which is used to extend the perceptual field,learn multilevel image features and improve the utilization of vessel information by the model; the downsampling and short connection sites are incorporated into the lightweight attention mechanisms and multi-channel attention module to increase the recognition of vessels by the model and reduce the possibility of mis-segmentation.Experiments are conducted based on two publicly available datasets,DRIVE and STARE,to verify the segmentation capability of the improved model.The results show that the accuracy is 0.965 2 and 0.971 5 on both data,and the sensitivity is 0.820 5 and 0.825 6,respectively,which are more advantageous than other algorithms in terms of segmentation performance.