To solve the supply-demand imbalance between service vehicles and passengers， improve the operational efficiency and profit of service vehicles， and reduce passengers' waiting time as well as improve their satisfaction with the service platform at the same time， a Deep Aggregation Neural Network （DANN） model was proposed for predicting the demand of online car-hailing aiming at the multi-dimensional spatio-temporal data with differentiated structures. Firstly， a period-based spatio-temporal variable classification method and a spatial variable classification method based on image point values were proposed by considering multi-dimensional influencing factors such as time， space， and external environment comprehensively. Secondly， different sub neural network structures were constructed to fit the nonlinear relationships between temporal， spatial， environmental variables and the demand respectively based on data characteristics. Thirdly， an aggregation method of multiple heterogeneous sub neural networks was proposed to simultaneously capture the implicit features of spatio-temporal data with different structures. Finally， a method of setting aggregation weights was analyzed to obtain the optimal performance of the network model. Experimental results show that the proposed model has the average error of R² on three real-world datasets of 9.36%， and compared with the Fusion Convolutional Long Short-Term Memory Network （FCL-Net） and Hybrid Deep Learning Neural Network （HDLN-Net） models， the proposed model has the R² increased by 4.6% and 5.22% on average respectively， and the Mean Square Error （MSE） reduced by 27.01% and 26.6% on average respectively. Therefore， DANN can greatly improve the accuracy of demand prediction in practical applications and can be used as an effective means of demand prediction for online car-hailing.