For the simulation of a trickle-bed reactor (TBR) in coal and oil refining, modeling the liquid maldistribution of the gas-liquid distributor incurs enormous pre-processing work and bears a huge computational cost. A closed-loop optimized system with computational fluid dynamic (CFD) data is therefore proposed for the first time in this paper. A fast prediction model based on support vector regression (SVR) is developed to simplify the modeling of the liquid flow rate in TBRs. The model uses CFD simulation results to determine an optimized set of structural parameters for the gas-liquid distributor in TBRs. In order to obtain an accurate SVR model quickly, the particle swarm optimization (PSO) algorithm is employed to optimize the SVR parameters. Then, the structural parameters corresponding to the minimum liquid maldistribution factor are calculated using the response surface methodology (RSM) based on the hybrid PSO-SVR model. The CFD validation results show a good agreement with the values predicted by RSM, with liquid maldistribution factors of 0.159 and 0.162, respectively.