In order to avoid creating or modifying the huge amount of finite element model of the rocket engine,application of the RBF（ radial basis function） neural network which was used to predict structural frequency of the liquid rocket engine was analyzed. A high pressure staged combustion LOX（ liquid oxygen） / kerosene rocket engine was taken as research object. By considering the material differences between the inner and outer wall of the nozzle,the precise nozzle finite element model was established using the stiffness and mass equivalence principle. Then the finite element model of staged combustion rocket engine was established using the method of distributed parameter and the finite element model was modified by modal experimental data.The difference structural parameters which were chosen according to the structural sensitivity analysis were considered as training samples. Then the RBF neural network was trained using these samples,and the structural frequency was predicted by the trained RBF neural network. The results show that the RBF neural network can predict structural frequency of the rocket engine accurately and the prediction error was less than 1. 0%. This method also has a fast convergence pace and it can be widely used in the field of the structural simulation of the rocket engine.