Abstract: Given the one-dimensional dynamic joint angle and axial static load difference of the coal rock structural plane under the bearing damage of the stress wave transmittance problem, the mechanism of interface inclination and axial static load on the transmitted stress wave of the coal-rock structural surface was revealed by using indoor experiments, theoretical analysis and computer simulation. The simulation and machine learning of stress wave transmission in the experimental process of Split Hopkinson Pressure Bar (SHPB) were carried out by combining the Barton-Bandis nodal ontology model, UDEC discrete element simulation and Gray Wolf Algorithm optimized BP neural network technology. The significance orthogonal test and multi-factor analysis of variance were carried out with full consideration of the various parameters, which screened out the main influencing factors and determined the correction scheme. Simultaneously, a machine learning model for the correction of Barton-Bandis intrinsic numerical simulation parameters under axial static loading and inclination differences of coal rock structural planes is obtained, which greatly improves the computational efficiency of the correlation mechanism between deformation behavior and intrinsic parameters in the impacted state of coal rock structural planes. This study demonstrates that the machine learning prediction model based on BP artificial neural network technology has well-applicability, which can quickly determine the model parameters under the current inclination angle and axial static load of the coal rock structural plane, provide an efficient data-driven correction method for the parameters of the Barton-Bandis intrinsic model of the coal rock structural plane and also predict the parameters of numerical simulation of the coal rock structural plane under the larger inclination angle and axial static load ranges other than the given training samples.