In recent years， semantic segmentation models based on Convolutional Neural Network （CNN） have shown excellent performance in a variety of applications. However， these models usually do not generalize well when they are applied to new domains， especially from synthetic to real data. The problem of Unsupervised Domain Adaptation （UDA） is attempting to train in a known domain with labeled data （the source domain） while learning in an unknown domain with unlabeled data （the target domain）， in order to improve the generalization ability of the segmentation model trained in the source domain to the target domain. The existing methods have made great progress through training pseudo-labels on unlabeled target domain images by self-training， and various ways have been proposed to reduce low-quality pseudo-labels brought by domain migration， but the above leads to mixed results. Aiming at this problem， a masking enhanced self-training domain adaptation method was proposed to generate pseudo-labels for target domain image masking enhanced processing and correct pseudo-labels generated from unmasked target images， and with the goal of minimizing loss of consistency between the pseudo-labels of masked image and the corrected pseudo-labels of unmasked image， more features of the target domain were learnt by the model and more robust pseudo-labels were generated by the model at the same time. Experimental results show that the proposed method achieves good performance in benchmark experiments of semantic segmentation used commonly in two UDA tasks， GTA5 （Grand Theft AutoV） → Cityscapes and SYNTHIA （SYNTHetic collection of Imagery and Annotations） → Cityscapes. Specifically， compared with the classical DACS （Domain Adaptation Cross-domain Sampling） method， the proposed method improves the mean Intersection over Union （mIoU） by 1.3 percentage points on the GTA5 dataset， and 1.2 percentage points on SYNTHIA dataset. In addition， the ablation experimental results show the effectiveness of the proposed mask enhancement and pseudo-label correction modules. It can be seen that the proposed self-training domain adaptation method learns more target domain context information and improves generalization ability of the segmentation model in target domain.