Graph convolutional network (GCN), with its capability to update the current node features according to the features of its first-order adjacent nodes and edges, has achieved impressive performance in dependency capturing. But some important nodes from which we should figure out the dependencies are not first-order reachable, which calls for multi-layer GCNs for indirect relevance capturing. In this paper, we propose a novel weighted graph convolutional network by constructing a logical adjacency matrix which effectively solves the feature fusion of multi-hop relation without additional layers and parameters for relation extraction task. And we apply an Entity-Attention mechanism to enrich the entity pairs with more focused semantic information. Experimental results on TACRED and SemEval 2010 task 8 show that our model can take better advantage of the structural information in the dependency tree and produce better results than previous models.