Abstract: The hydrodenitrogenation (HDN) experiments of five low-quality feeds of mixed gasoline and diesel were carried out in an 100 mL hydrogenation device under the conditions of using Ni-Mo-P/Al2O3 catalyst, with reaction temperature of 320—360 ℃, space velocity of 1.2—2.0 h-1, hydrogen to oil ratio of 350—550 and reaction pressure of 6—8.5 MPa. Based on the experimental data, the prediction models for HDN rate of these mixed feeds were established by BP neural network and RBF neural network respectively. The calculation results show that the average relative error of BP neural network in predicting HDN rate is 3.42%, which of RBF neural network is 2.58%. Both of them could meet the industrial prediction requirements, however, the prediction performance of that by RBF neural network seems better. The effects of feed properties and process conditions on HDN rate of mixed feeds are further studied with RBF neural network. The sequence of feed properties affecting HDN rate is as follows: sulfur content > density > nitrogen content > 50% distillation point > viscosity > bromine value. The sequence of process conditions affecting HDN rate is as follows: temperature > space velocity > pressure > hydrogen to oil ratio. These results are helpful to optimize the HDN process conditions for treating mixed feeds of gasoline and diesel.