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With the explosive growth of products and multimedia contents on the Internet, the desire of users to find these online resources matching their interests makes it imperative to develop high quality recommendation systems. In this paper, we propose one novel neural network, namely Dynamic Heterogeneous Graph Convolutional Network (DHGCN), for item recommendation. Specifically, our proposed DHGCN consists of two components, namely the graph learner and heterogeneous graph convolution. The graph learner considers not only the user–item interactions but also user–user and item–item interactions, which are dynamically established during the graph evolution process. The heterogeneous graph convolution relies on a novel cross gating strategy to aggregate the representations yielded by the convolution over the learned heterogeneous graph and the item content information. Through comprehensive experiments on two real-world datasets, the proposed model is demonstrated to be effective on item recommendation task, outperforming the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]