Aiming at the characteristics of small retinal vessels and complex scale changes, a multi-level adaptive scale U-shaped retinal vessel segmentation algorithm is proposed. Firstly, the residual module is introduced based on the encoder-decoder structure to enhance the channel feature propagation capability. Secondly, a multi-scale feature extraction module is embedded at the bottom of the network to adjust the receptive field to effectively extract multi-scale features. At the same time, an improved adaptive feature fusion module is added to the skip connection part to promote effective fusion between adjacent hierarchical features to extract more small blood vessel features. Finally, the multi-level attention structure output on the setting side of the decoding part performs adaptive refinement on the multi-level features. The experimental results show that the accuracy of the algorithm on the DRIVE, STARE and CHASEDB1 datasets reaches 0.9645, 0.9694 and 0.9671, respectively, the sensitivity reaches 0.8417, 0.8465 and 0.8545, and the AUC reaches 0.9866, 0.9908 and 0.9877, respectively, and the overall performance is better than the existing algorithms.