To improve the accuracy of aircraft wake detection and inversion based on LiDAR, an estimation method for atmospheric background turbulence dissipation rate (EDR) on the basis of Kolmogorov structure function is established according to the scanned radial wind speed data. Then, on the strength of a probabilistic two-phase wake vortex Decay and Transport Model, the influence of turbulence on the wake dissipation process is taken into account to achieve the prediction of wake intensity circulation and vortex core motion trend based on historical detection data. By combining environmental detection data with predictive models, the inversion accuracy of wake characteristic parameters is improved. The research is shown that tail vortex trajectories predicted using the model in this paper are 59.5% and 64.8% more accurate in terms of radial distance and angle, respectively compared to the inverse algorithm.