This study investigates multi-factor dynamics in vertical roller mill separator using a BP neural network-Response Surface Methodology (BP-RSM) framework. Computational Fluid Dynamics (CFD) with a Dense Discrete Phase Model (DDPM) simulates particle motion, generating 60 datasets. The BP neural network achieves an integrated determination coefficient of 97.05% on these data, expanding to 12,150 virtual experiments. The relationships of the data generated by the BP neural network were expressed through RSM, realizing an explicit and visual representation of multiple factors (with an integrated determination coefficient of 99.76% and a predicted determination coefficient of 99.75%), therefor the validation determination coefficient of BP-RSM joint fitting is 96.82%. BP-RSM surpasses traditional RSM in generalization and interpretability. The results indicate that the air injection velocity (vg), rotor rotation speed (ω), and rotor diameter ratio (d/d0) are significant factors influencing the separation size (dc). The variation in the rotor diameter ratio (d/d0) can modulate the intensity of vortices generated between moving blades, which significantly influences the adaptation to fluctuations in air injection velocity (vg). In the target particle size range of 40-60 μm, the rotor diameter ratio (d/d0) of separator is between 1.2 and 1.4. This configuration can enhance the adaptability of the equipment's air injection velocity (vg) by a factor of 2-3 times. This framework effectively addresses the challenges associated with multifactor coupled modeling and the explicit representation of high-dimensional nonlinear relationships. It offers a solution for the multi-objective optimization of separators that is easy to operate, while ensuring both predictive accuracy and physical interpretability.