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Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) by meshing plentiful pixel-level labeled data, but data sparsity of high grid granularity in spatial perspective and limited learning capabilities of long-term dependencies in temporal perspective are still two striking bottlenecks. To address these problems, we propose a novel virtual graph modeling approach to focus on significant demand regions and a novel Deep Multi-View Spatio-temporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen the learning capabilities of spatial dynamics and long-term temporal dependencies. Specifically, DMVST-VGNN integrates structures of 1D CNN, Multi-Graph Attention Neural Network and Transformer Network, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, multiple experiments are conducted on two large-scale New York City datasets in higher granularity prediction scenes. And the experimental results demonstrate the effectiveness of DMVST-VGNN framework in ride-hailing demand prediction, no matter in spatial scale or the temporal scale. [ABSTRACT FROM AUTHOR]