To address the multidimensional threats posed by the U.S. air force's penetrating counter air （PCA） operational system to China's air defense security， this study resolves the evaluation bias caused by the imbalance between subjective and objective weights in traditional threat assessment methods by innovatively constructing a “combined weighting TOPSIS” evaluation model. An assessment framework encompassing seven core metrics—including collaborative command and control effectiveness， electromagnetic suppression capability， and fire strike effectiveness—is established based on PCA operational characteristics. Subsequently， the entropy weight method is employed to quantify objective weights for platform intrinsic attributes， while the eigenvector method is utilized to derive subjective weights reflecting tactical intent. A game theory-based deviation minimization algorithm is introduced to achieve Nash equilibrium optimization of the combined subjective-objective weights. PCA platform threat assessment model is developed using the technique for order preference by similarity to ideal solution （TOPSIS）， enabling the prioritization of PCA platform threats. Case simulation results validate the effectiveness of this threat assessment methodology， which provides a reference framework for evaluating incoming PCA platform threats in counter-penetration operations.