Retinal blood vessels are small and complex. When segmentating retinal blood vessels, noise, fracture and undersegmentation often occur. To solve this problem, a lightweight network named LRU-Net based on local feature enhancement is proposed to capture more features of small blood vessels. Firstly, a feature extraction module is added to the channel attention module to extract secondary features from input features so as to obtain more detailed features. Secondly, a feature fusion module is designed, which can fuse the high and low features more effectively in the decoder, and strengthen the final feature representation. Finally, a context aggregation module is designed to extract multi-scale information with different resolutions of the deepest features, and then splicing it to make the input features into the upper sampling more detailed. Experimental results on FIVES and OCTA-500 data sets show that compared with U-Net, the proposed method not only achieves lightweight, but also improves the accuracy and Dice coefficient of retinal vessel segmentation to a certain extent.