Abstract: The aerodynamic force measurement technology during the acceleration phase of the motion model is one of the key technical challenges in the maglev flight wind tunnel. The aerodynamic force measurement balance is subjected to various strong interferences such as inertial forces in the acceleration section, which masks the measured aerodynamic force values. In order to restore the aerodynamic force signal under the harsh conditions of strong interference and low signal-to-noise ratio, this study proposed an aerodynamic force interference stripping algorithm based on deep learning. Firstly, the short-time Fourier transform was applied to the balance signal to determine the spectral characteristics of each interference, which was used as the input of the algorithm model. Then, an "encoder-decoder" architecture model was constructed to extract features from the complex signals measured by the balance and accurately reconstruct the desired aerodynamic force signal. After a comprehensive evaluation on the test set, the proposed algorithm demonstrated excellent performance in aerodynamic interference stripping, achieving a corresponding reconstruction accuracy of approximately 92.7% for the drag, lift, and pitching moment components. This study provides strong support for the aerodynamic force measurement in the future test environment of the maglev flight wind tunnel.