The effective representation of business processes is a key problem of predictive monitoring based on deep learning networks. Most of the existing business process representation methods are unable to accurately reflect the characteristics and node information aggregation of events in the graph structure. Therefore, a business process representation method based on graph convolutional network is proposed. Firstly, the diagram structure is constructed based on the same prefix window event of the business process. Secondly, on the basis of the constructed graph structure data, the node vectorization representation based on graph convolutional network model is given, and the information is aggregated through the graph convolutional network. Then, the depth representation of nodes is applied to the downstream prediction task of the business process. Finally, five real datasets are used for experimental evaluation. The experimental results show that the proposed event log graph construction strategy and process representation method are effective in business process prediction. The source code of this work is available at https://github.com/LinZhang0/GCNp .