Due to the limitation of the existing retinal vessel segmentation methods that have insufficient segmentation ability for microvessels and capillaries,which leads to vessel disconnections and end vessel misses,resulting in poor retinal vessel segmentation performance,a multi-scale consistency and attention mechanism U-Net (MCAU-Net) is proposed.Firstly,the network embeds an attention refinement module (ARM) in the bottleneck feature layer,which can effectively refine the redundant features in the bottleneck layer and suppress the weights of irrelevant pixels,such as the background pixels.Moreover,the context fusion module (CFM) is combined with the traditional skip connection as a way to supplement the information gradually lost during the phase of feature extraction and strengthen the network′s ability to construct microvessels and capillaries.Finally,a multi-scale consistent training method is designed based on the multi-scale output of the network to enhance the sensitivity of the network to different scale features.The comparison experiments on DRIVE and CHASE_DB1 public datasets show that the network in this paper has good segmentation performance.