In view of the problem that quality of pseudo-labels is uneven in the current retinal vessel segmentation task and it requires to be screened to obtain the high-quality pseudo-labels,a novel semi-supervised deep learning framework for retinal vessel segmentation is proposed in this paper.The framework adopts the idea of divide and conquer to process data.Traditional deep learning methods are utilized especially for the labeled data,while Mean teacher model is used to deal with the unlabeled data.By comparing the different morphological outputs of the same input,the model can learn the common features between the unlabeled data and avoid the screening process brought by pseudo-label technology.Three benchmark networks,u-neural networks (U-Net),Dense-Net and Ladder-Net are put into the framework,the experiments are carried out on DRIVE and CHASEDB1 datasets,which achieve good segmentation results.It shows that the framework can improve the ability of the network to distinguish different threshold pixels.