In this paper,an end-to-end neural network-based nested retinal vesse l segmentation model algorithm (MS-UNet++) is proposed to solve the problem of h igh segmentation difficulty caused by the difficulty of extracting subtle vessel features in retinal images.The algorithm selects the deep supervision network UNet++ as the segmentation network model to improve the use efficiency of featur e.In our algorithm design,We choose to introduce MulitRes module for feature e xtraction to improve the feature learning effect of the small blood vessels in l ow contrast environment.And after feature extraction,we select to insert SENet module is added for squeezing and exciting operations,so as to enhance the rec eptive field in feature extraction stage and increasing the weight of target-re l ated feature channels.The experimental results based on DRIVE image data set sh ow that the overlap ratio DICE value between with the segmentation result and th e ground truth is 83.64%,the parallel ratio IOU is 94.83%,and the accuracy is 96.79%,and the sensitivity is 81.78%,compared with the existing algorithm mode ls,the results of our algorithm models has a certain improvement.As a result, our algorithm models can be used for retinal image vessel segmentation and provi de auxiliary information for clinical diagnosis.