Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reason- ing based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first con- struct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the exist- ing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outper- forms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly effi- cient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance.