Lymphomas are a group of malignant tumors developed from lymphocytes, which may occur in many organs. Therefore, accurately distinguishing lymphoma from solid tumors is of great clinical significance. Due to the strong ability of graph structure to capture the topology of the micro-environment of cells, graph convolutional networks (GCNs) have been widely used in pathological image processing. Nevertheless, the softmax classification layer of the graph convolutional models cannot drive learned representations compact enough to distinguish some types of lymphomas and solid tumors with strong morphological analogies on H&E-stained images. To alleviate this problem, a prototype learning based model is proposed, namely graph convolutional prototype network (GCPNet). Specifically, the method follows the patch-to-slide architecture first to perform patch-level classification and obtain image-level results by fusing patch-level predictions. The classification model is assembled with a graph convolutional feature extractor and prototype-based classification layer to build more robust feature representations for classification. For model training, a dynamic prototype loss is proposed to give the model different optimization priorities at different stages of training. Besides, a prototype reassignment operation is designed to prevent the model from getting stuck in local minima during optimization. Experiments are conducted on a dataset of 183 Whole slide images (WSI) of gastric mucosa biopsy. The proposed method achieved superior performance than existing methods.Clinical relevance- The work proposed a new deep learning framework tailored to lymphoma recognition on pathological image of gastric mucosal biopsy to differentiate lymphoma, adenocarcinoma and inflammation.