Aiming at the problems of computational redundancy and difficulty in segmenting fine structures of the original U-Net in medical image segmentation tasks， an improved U-Net algorithm based on attention mechanism and multi-scale fusion was proposed. Firstly， by integrating channel attention mechanism into the skip connections， the channels containing more important information were focused by the network， thereby reducing computational resource cost and improving computational efficiency. Secondly， the feature fusion strategy was added to increase the contextual information for the feature maps passed to the decoder， which realized the complementary and multiple utilization among the features. Finally， the joint optimization was performed by using Dice loss and binary cross entropy loss， so as to handle with the problem of dramatic oscillations of loss function that may occur in fine structure segmentation. Experimental validation results on Kvasir_seg and DRIVE datasets show that compared with the original U-Net algorithm， the proposed improved algorithm has the Dice coefficient increased by 1.82 and 0.82 percentage points， the SEnsitivity （SE） improved by 1.94 and 3.53 percentage points， and the Accuracy （Acc） increased by 1.62 and 0.04 percentage points， respectively. It can be seen that the proposed improved algorithm can enhance performance of the original U-Net for fine structure segmentation.