Automated security inspection is an effective measure to maintain public safety and improve the efficiency of security inspection. Usually, it is difficult to obtain enough labelled samples which contain some prohibited items of rarely appearing. Furthermore, the category of prohibited items varies in different scenarios and security levels. A graph matching network algorthm for few-shot prohibited item segmentation was introduced to deal with the imbalance of training samples faced by neural network methods, and to inspect prohibited items of new categories without the requirement of retraining. This model parallelly input a query image and several support images into the graph matching network, and segmented the prohibited items from the query image according to the matching results. The graph matching module not only considered the matching problem from the point of node similarity between two graphs, but also establisheed a global concept to match the graphs with the use of DeepEMD algorithm. Experiments on the SIXray dataset and Xray-PI dataset show that proposed model achieves 36.4% and 51.2% meanIoU for 1-shot tasks and outperforms the state-of-the-art method by 2.5% and 2.3% meanIoU, respectively. The extended experiments demonstrate that propoed algorithm can effectively improve the accuracy of few-shot X-ray image segmentation.