Compared with sentence-level relation extraction, it is more common that entities involving relationships exist in multiple sentences in actual scenarios. Therefore, document-level relation extraction has gradually become a research hotspot in the field of information extraction in recent years. In order to make full use of the contextual information and structure information of the document, this paper combine embedding representation of entities and the explicit structural relationships between entities to study extraction of document-level inter-sentence relations. Firstly, document is encoded using BiLSTM, while document graph is established using the explicit structure of document; Secondly, relational graph convolutional neural network based on message propagation is used to update nodes in the graph, thus local information of document is integrated into node embedding representation, using which edge embedding is updated; Finally, an iterative algorithm is used to complete the reasoning of edge information, and entity relation is predicted using a feed-forward network based on edge embedding. Experimental results show that, compared with the EoG model, the F1 values of inter-sentence relation are increased by 2.6% and 0.8% on the CDR and GDA datasets, respectively.