To enhance the aerodynamic parameter identification capabilities of morphing air-craft under complex flight conditions, this paper proposes a modeling method based on Physics-Informed Neural Networks (PINNs). By embedding physical constraint equations derived from aircraft dynamics into the loss function of the neural networks, the approach combines data-driven learning with prior physical knowledge, enabling efficient identification related parameter of aerodynamic forces and moments. The framework integrates both Computational Fluid Dynamics (CFD) data and flight simulation data to construct a unified model applicable to various flight configurations. This method ensures physical consistency and strong generalization ability, maintaining high modeling accuracy even under limited data conditions. Compared with traditional empirical or purely data-driven models, the proposed approach significantly reduces the dependence on large-scale experimental data and improves interpretability.