Aiming at the problem of degraded measurement accuracy in air data systems （ADS） caused by airflow separation and shock wave interference during complex flight conditions such as transonic regimes and high maneuverability, this paper proposes an atmospheric parameter reconstruction method based on datadriven wind field estimation combined with velocity vector triangle resolution. Utilizing historical flight data, the method employs a least squares polynomial fitting algorithm for wind speed prediction and leverages a velocity vector triangle model to inversely compute true airspeed, thereby reconstructing key atmospheric parameters including angle of attack (AOA), angle of sideslip (AOS), and Mach number. Simulation results demonstrate that the proposed method achieves significant accuracy improvement in atmospheric parameter reconstruction under complex conditions: The original AOA measurements fluctuate around 2°, while the original AOS measurements fluctuate around 1°. Evaluated using mean absolute error (MAE) and root mean squared error (RMSE) metrics, the errors are 0.47° (MAE) and 0.50° (RMSE) for AOA, 0.20° (MAE) and 0.24° (RMSE) for AOS， 0.005 5 (MAE) and 0.006 3 (RMSE) for Mach number. Additionally, the study compares the proposed method with an atmospheric parameter estimation approach based on the frozen wind field theory, with both methods effectively reducing measurement errors in AOA and AOS. This research provides a software solution for high-precision, highly reliable air data measurement in complex flight environments without requiring additional hardware.