Abstract: Detailed information of flow fields is of great significance for designing and optimizing multiphase flow systems. However, predicting spatiotemporal evolution of gas-solid flows using numerical simulation often requires a significant amount of computation and time. In this study, we proposed a 3D convolutional neural network for predicting reactive dense gas-solid flows. We first explored the design of model architecture and extensively evaluated the performance in terms of efficiency, accuracy, long-term prediction stability and generalizability for a non-reactive fluidized bed. Then we extended the method to a biomass fast pyrolysis process. The proposed model achieves real-time prediction, 3–4 orders of magnitude faster than CFD-DEM simulations. The surrogate model reasonably captures bubble-driven flow behaviors and effects of bubble on fast pyrolysis reactions. The predicted bubble characteristics, and time-averaged and RMS flow fields match well with the simulation results. Our approach exhibits excellent long-term stability and has good generalization capability to unseen fluidization velocities. To the best of our knowledge, this is the first time a neural network has been successfully applied to learn spatiotemporal evolution of reactive dense gas-solid flows.