Abstract: In the design and optimization of structural cross-sections, the efficient and accurate evaluation of aerodynamic performance is of significant importance. Traditional aerodynamic evaluation methods, such as wind tunnel tests and computational fluid dynamics numerical simulations, are time-consuming and costly. This is particularly evident when dealing with iterative computational tasks involving numerous design parameters and uncertain optimization trajectories for structural cross-sections, as the inefficiency of traditional methods becomes more pronounced. To address these challenges, this study proposes an intelligent prediction method based on a deep learning surrogate model, focusing on the rapid and accurate prediction of flow fields around two-dimensional combined bluff body cross-sections and static force coefficients under steady wind conditions. Specifically, this method employs a unified image-like shape representation to characterize the aerodynamic shapes of bluff body combined cross-sections, ensuring broad applicability without being limited by specific cross-section configurations. By integrating convolutional attention mechanisms and residual modules to construct the neural network architecture and using mean squared error to capture prediction errors, the method achieves a highly nonlinear mapping from aerodynamic shapes to flow characteristics and static force coefficients. The results demonstrate that, under steady wind, the prediction errors for the time-averaged flow fields, surface pressure distributions, and static force coefficients of two-dimensional combined bluff body cross-sections are all within 3.7%, 0.35%, and 6.25%, respectively, thereby satisfying the accuracy requirements. Additionally, the computational efficiency is improved by four orders of magnitude compared to conventional methods. Consequently, this method provides an efficient and practical solution for the rapid prediction of aerodynamic performance for bluff body cross-sections under steady wind environments.