Abstract: The shape of aircraft with ‌telescopic deformed structures‌ is complex, and the distribution of ‌aerothermal‌ data varies ‌significantly‌. Traditional surrogate models to capture the ‌aerothermal‌ heating distribution of ‌telescopic structures‌, hindering effective prediction struggle on structural surfaces. Based on ‌the conditional diffusion model, ‌the Heating-MLP Diffusion (HMD) method for deformable structures‌ was proposed, comprising two processes: forward diffusion and reverse denoising. In the forward diffusion process, the original ‌aerothermal‌ data is gradually ‌corrupted‌ until it becomes pure Gaussian noise. In the reverse denoising process, ‌using‌ the shape and operating conditions of the deformed structure as ‌conditional inputs‌, a fully connected neural network predicts the noise added at each diffusion step, thereby learning the implicit ‌distribution characteristics of aerothermal‌ heating data, thus enabling ‌the prediction of aerothermal heating‌ on the surface grid points of ‌aircraft telescopic deformed wings‌. Numerical simulation data validated the proposed model. Experimental results demonstrate that compared with Gaussian processes, neural processes, and neural networks, the ‌conditional diffusion model-based‌ aerothermal prediction method achieves higher accuracy, with a mean absolute percentage error of ～10%. This evidence proves its effectiveness in predicting ‌aerothermal heating‌ on ‌high-speed aircraft‌ wings with telescopic deformed structures providing an accurate prediction model for engineering applications.