Aiming at the slow detection speed of traditional retinal blood vessel segmentation algorithm, it is difficult to apply to real-time medical aided diagnosis system, a lightweight retinal blood vessel segmentation model based on Shuffle-Unet is proposed. In order to simplify the model structure, the lightweight model ShuffleNetV2 is structurally pruned, and the last convolutional layer, global pooling layer and fully connected layer on the ShuffleNetV2 structure are pruned; In order to reduce the computational complexity of the model and improve the running speed of the model, the pruned ShuffleNetV2 is used as the backbone extraction network of the model; Use the random channel separation operation module to build an upsampling model structure to enhance the network feature transfer capability; The attention mechanism module is used to fuse the output of the first feature layer and the upsampling layer of the model to enhance the extraction of effective features from the two dimensions of the channel and the space. By comparing the two public datasets DRIVE and CHASE_DB1 with other retinal blood vessel segmentation algorithms, it effectively proves that the Shuffle-Unet model has the characteristics of high segmentation accuracy and high detection speed.