Heterogeneous graph containing different types of nodes or links is one of graph types, which is most relevant to actual problems. However, the research for heterogeneous graph has not been studied adequately. In this paper, we propose a new model named Heterogeneous Graph Gated Attention Network (HGGAN) to process heterogeneous graph, including node feature space unification, center-neighbor nodes (C-N) aggregation and metapath-metapath (M-M) aggregation. Especially, we use multihead attention mechanism in C-N aggregation. Owing to the contribution of each attention head is different, so we use a convolutional sub-network to assign a parameter to reflect the contribution of different attention heads. Experimental results on three real-word heterogeneous datasets show that HGGAN achieves state-of-the-art results on node classification task.