In this study, an aircraft laser velocimetry frequency estimation model based on fast Fourier transform (FFT) is proposed to improve the accuracy of airspeed measurement in flight safety. By analyzing the datasets of four different flight stages, the model shows good frequency estimation accuracy and robustness in noise processing and intermittent receiving mode. The research also involves the estimation of noise characteristics and how to use autocorrelation and spline interpolation to estimate signal frequency without amplitude and phase information. Although the model has limitations in the dependence of the known signal model and the performance in extreme environments, this study not only provides a new frequency estimation method for aircraft laser velocimetry, but also has important practical application value for improving flight safety. Future work will focus on integrating machine learning techniques and developing advanced noise reduction algorithms to further improve the practicality and reliability of the system.