Aircraft wake, an inevitable byproduct of aircraft flight, poses a major threat to aviation safety and limits the improvement of aviation efficiency and capacity. Accurate identification of aircraft wake vortex nuclei is a prerequisite for dynamically reducing wake intervals, and coherent Doppler Lidar (CDL) is the main tool for clear-air wake detection. To address the significant errors in identifying and inverting key parameters of aircraft wake turbulence caused by the limitations of CDL spatiotemporal resolution and background wind field effects, this study proposes a wake vortex parameter inversion model based on Bayesian network (BN) and mean squared error (MSE) using CDL detection data. An atmospheric background wind and turbulence environment are built and superimposed onto the simulated wake velocity field to obtain a simulation dataset for training the model. The results show that the proposed model can obtain parameter inversion results with small errors (within 2 meters deviation of the vortex core position and within 5% deviation of the ring volume in the simulated case) at an acceptable computational level. In actual cases, the mean squared error of the inversion velocity field is significantly reduced (more than 50% on average) compared with the conventional algorithm. This research can be used for real-time monitoring of wake vortices at airports and is of great significance for the development of wake interval standards.