Automatic Xray security inspection is an important method to maintain public safety. Current research of prohibited item inspection on Xray images only works on predefined classes in the dataset and cannot be generalized to unseen categories. The imbalance problem in the dataset will also affect the performance of models. In order to solve above defects, the paper proposes a segmentation model for prohibited item inspection in Xray Images based on fewshot learning. The model first embeds the test image and annotated support images to a common space, then measures the spatial pixelwise similarity and regional similarity, finally segments out suspected areas in the test image. To deal with uncertain numbers of support images, a fusion method based on the ConvGRU is proposed to integrate the similarity information for the test image and different support images. Experiments show that the proposed model improves 20% and 22% meanIoU compared to the stateoftheart methods under 1shot task and 5shot task, which demonstrates the ability to recognize unseen categories.