In view of the problem that quality of pseudo-labels is uneven in the c urrent retinal vessel segmentation task and it requires to be screened to obtain the high-quality pseudo-labels,a novel semi-supervised deep learning framew ork for retinal vessel segmentation is proposed in this paper.The framework adopts the idea of divide and conquer to process data.Traditional deep learning metho ds are utilized especially for the labeled data,while Mean teacher model is use d to deal with the unlabeled data.By comparing the different morphological outp uts of the same input,the model can learn the common features between the unlab eled data and avoid the screening process brought by pseudo-label technology.T h ree benchmark networks,u-neural networks (U-Net),Dense-Net and Ladder-Net are put into the fra mew ork,the experiments are carried out on DRIVE and CHASEDB1 datasets,which achi eve good segmentation results.It shows that the framework can improve the abil ity of the network to distinguish different threshold pixels.