Abstract: To enhance the independent development capability and efficiency of new strategic aircraft, it is crucial to accurately acquire aerodynamic data over a wide range of high Reynolds numbers. There is an urgent need to improve the accuracy of aerodynamic simulations for complex flow conditions at flight Reynolds numbers, and to address the high cost associated with ground testing in cryogenic wind tunnels. The experimental data at high Reynolds numbers are few and sparse, and there are serious data imbalance and few-shot problems in the accurate aerodynamic modeling under wide-range Reynolds numbers. To solve the contradiction between the cost and accuracy of the aerodynamic model under variable Reynolds number, and focus on the cost reduction and efficiency increase of subsequent aircraft design, this study takes the CHN-T1 transport aircraft as the research objective. Based on data fusion and information transfer techniques, we achieve the rapid prediction of variable Reynolds number aerodynamics through few-shot learning. This approach reduces the reliance on high Reynolds number samples in the modeling process. In the present study, a wide-ranging variable Reynolds number aerodynamic dataset is constructed using a combination of 18 aerodynamic curves. These curves encompass sub-transonic speeds and variable Reynolds numbers ranging from millions to tens of millions. Additionally, various complexity cases are designed for demonstration. As a verification, a benchmark is established using high-fidelity experimental data through a single source method. The characteristics of different methods are then compared. The results demonstrate that when approximately 10 high-fidelity aerodynamic curves are utilized as modeling data, the data fusion neural network reduces the root mean square error of aerodynamic modeling by over 50%. Additionally, the information transfer method reduces the error by at least 40%.