Abstract: To address the insufficient ride-pooling order correlations in existing ride-pooling demand prediction, this paper proposes a spatiotemporal prediction model for ride-pooling origin-destination demand based on a spatiotemporal multi-graph convolutional neural network. First, ride-pooling order data is processed into time-series data representing demand between origin destination points, and multiple characteristic features of ride-pooling demand are analyzed and extracted. Based on this, multiple semantic graphs reflecting ride-pooling order correlations are innovatively constructed using ride-pooling success rate, route similarity, and land-use similarity, with a novel semantic modeling perspective for ride-pooling demand prediction. Meanwhile, geographic graphs are built to capture adjacency and distance relationships, enabling a multidimensional modeling of geographical correlations and travel semantic associations. For model development, this paper proposes a hierarchical