In recent years, traffic flow prediction methods based on deep learning have been a hot research topic in the field of traffic flow prediction. Unlike traditional convolutional neural networks, graph convolutional networks, which are suitable for processing non-Euclidean data, have shown powerful capabilities in spatial feature modeling. Meanwhile, topological maps, distance maps and flow similarity maps that reflect the spatial characteristics of the road network are typical non-European data. Therefore, traffic flow prediction based on graph convolutional networks and its variants has become a research hotspot in the field of traffic flow prediction, and many attractive research results have been achieved. This article classifies and summarizes traffic flow prediction models based on graph convolutional networks in recent years. First, starting from the basic definition of graph convolutional network, the basic principles of graph convolution are described in detail by combining the definitions of spatial convolution and spectral convolution. Secondly, according to the network structure characteristics of the prediction model, the traffic flow prediction models based on graph convolutional network are divided into two major categories: "combined type" and "improved type". The most representative model structures are analyzed and discussed in detail. In addition, a review of typical data sets commonly used in the field of traffic flow prediction for model performance comparison is provided, and a simulation test is conducted using one of these real datasets to demonstrate the prediction performance of four traffic flow prediction models based on graph convolutional networks. Finally, based on the current research status and development trends, an open discussion and outlook on the future research hotspots and challenges in the field of traffic flow prediction methods based on graph convolutional networks are conducted.