Concerning the problems of inaccurate information of raw data， low quality of samples and poor generalisation ability of models in the field of Multiple-Choice Question Answering （MCQA）， a mask data augmentation model based on Graph Convolutional Network （GCN） was proposed， namely GMDA （Graph convolution network-based MASK Data Augmentation）. Using GCN as the basic frame， the words in the articles were abstracted as graph nodes and connected by Question-candidate Answer （QA） pair nodes to establish connections with related article nodes. Secondly， the similarity between nodes was calculated and the masking technique was applied to mask the nodes in the graph to generate the augmented samples. Thirdly， the augmented samples were subjected to feature expansion by using GCN to enhance the model's information representation capability. Finally， a scorer was introduced to score the original and augmented samples， and the curriculum learning strategy was combined to improve the accuracy of answer prediction. The results of the comprehensive evaluation experiments show that compared with the best baseline model EAM on RACE-M and RACE-H datasets， the proposed GMDA model improves the accuracy by an average of 0.8 and 0.4 percentage points respectively， and compared with the best baseline model STM （SelfTraining Method） on DREAM dataset， the GMDA model has the average accuracy improved by 1.4 percentage points. Besides， comparative experiments also prove the effectiveness of the GMDA model in MCQA tasks， which can help further research and application of data augmentation techniques in this field.