With the development of the times, online car-hailing has gradually become an important mode of travel in today’s society. This new travel mode greatly reduces the travel costs and makes people’s lives more convenient. Online car-hailing demand forecast is an important part of the artificial intelligence transport system and has high application value. However, traditional research ignores the impact of the social attribute similarity between the destination and different regions when modeling, making the characteristics of the models incomprehensive and the forecast accuracy of the algorithms low. In response to the above problems, a Multi-Graph Spatial-Temporal Graph Convolution Neural network (MGSTGCN) is proposed to solve the forecast problem of online car-hailing demand. The network consists of spatial and temporal components. The network associated with spatial problems models the similarity of geographic information, mobile information, and social attributes through graph convolution, and the temporal problems are processed by combining the attention mechanism with the LSTM network. In the experiments, we comparatively analyze the proposed model with four mainstream network models, and the results show that this model can more effectively capture the spatial-temporal characteristics of online ride-hailing demand data and increase the forecast accuracy.