Abstract: Acquiring extensive aerodynamic performance data in the current wing design phase incurs substantial costs, necessitating the development of efficient and accurate predictive methods. This study focused on a low-speed double-trapezoid wing. A dataset under subsonic flight conditions was constructed using OpenVSP and CFD simulations. Based on this dataset, a PointNet-based aerodynamic performance prediction network (PointNet-AP) was designed, which incorporated transfer learning utilizing multi-fidelity data. The PointNet-AP network took wing point cloud data, conventional planar geometric features, and flight conditions as inputs to predict the corresponding lift coefficient (C_L), drag coefficient (C_D), and pitching moment coefficient (C_m). Experimental results demonstrated that the PointNet-AP model with transfer learning achieved satisfactory predictive performance, with mean relative prediction errors of 2.88%, 4.75%, and 6.74% for C_L, C_D, and C_m, respectively.The proposed network provides a technical foundation for developing large-scale aerodynamic performance prediction models by offering solutions for data feature extraction and model transfer.