In the study of production and processing technologies such as mechanical shelling, sowing and planting of Camellia oleifera seeds, due to the lack of accurate discrete element simulation models and parameters, the simulation and actual errors of design equipment are large. Reverse engineering techniques were used to establish a discrete element model of Camellia oleifera seeds in EDEM software. 〖JP2〗Through physical tests, the angle of repose (AOR) of Camellia oleifera seeds was measured to be (27.93±1.46)°. The parameter intervals of density, collision recovery coefficient and static friction coefficient between camellia seed and plate were measured. The discrete model parameters of Camellia oleifera seeds were filtered by using the Plackett-Burman Design to obtain the parameters that had a significant impact on the AOR. The path of steepest ascent method was carried out to determine the optimal value range of the parameters. The central composite design (CCD) response surface method (RSM) and machine learning were used to establish the regression models involving the AOR and the significant parameters. The results showed that the predictive ability and stability of BP artificial neural network based on genetic algorithm (GA) were better than that of random forest, support vector regression and BP artificial neural network. GA optimization was used to obtain the static friction coefficient between seeds, which was 0.443, the static friction coefficient between seeds and steel plates was 0.319, and the rolling friction coefficient between seeds was 0.063. The simulated AOR was measured to be 27.63°, and the relative error from the actual AOR was 1.09%. RSM optimization was used to obtain the static friction coefficient between seeds, which was 0.383, the static friction coefficient between seeds and steel plates was 0.335, and the rolling friction coefficient between seeds was 0.064. The simulated AOR was measured to be 26.99°, and the relative error from the actual AOR was 3.33%. The results showed that GA-BP-GA had better parameter optimization effect than RSM in the parameter calibration of Camellia oleifera seeds. Moreover, the built model and parameter calibration results of Camellia oleifera seeds can be used for discrete element simulation research.