Aircraft is one of the most important inventions in the early 20th century, which gave birth to the development of air transport industry and changed the transportation, economy, production and daily life of human beings. Subsequently, aviation safety issues have gradually become the focus of attention. The emergence of heavy aircraft, especially the emergence of B747-100, makes the aviation safety problems caused by aircraft wake vortex can not be ignored. Accurate identification of the position of the aircraft wake vortex core is the basis for dynamically reducing the wake vortex spacing. At present, the vortex core position extraction and vortex intensity evaluation based on coherent Doppler pulse lidar observation data have been realized with the algorithm. However, the interference of the background wind field and the need for calculation speed are major technical difficulties for the application of the existing wake vortex identification algorithm. In order to further improve the environmental adaptability and calculation accuracy of the wake vortex identification algorithm, this paper conducts corresponding research.Aiming at the requirement of near real-time output of lidar wake vortex detection results and the problem of inaccurate vortex core positioning in the near-ground environment of existing algorithms, this paper optimizes the previous identification methods. By referring to the tangential velocity method and the concept of ROI (Regions Of Interest), the region is focused to reduce the introduction of interference. By referring to the radial wind speed method, the rotation characteristics of the wake vortex are introduced into the solution to improve the anti-interference ability of the algorithm. By referring to the fast identification method, the spectral width information is combined with the radial wind speed information to improve the accuracy of the vortex core location. The path integral method is used to reduce the influence of ground effect on the solution results. By referring to the optimization method, the verification conditions of the solution results are set up to improve the reliability of the solution results. Aiming at the problem that the deviation of some circulation calculation results in lidar wake vortex detection is too large, this paper optimizes from two aspects. First, for the circulation calculation deviation caused by the movement of the wake vortex during the scanning process, this paper proposes a new correction method, that is, the displacement of the wake vortex is calculated to correct the velocity distribution of the wake vortex, and then the circulation of the wake vortex is corrected; Secondly, the error sources in the correction process of the idealized aircraft wake vortex model are analyzed to reduce the introduction of errors in the fitting process.The calculation time of this method on the standard personal computer is usually less than 2 s, and it is verified by the observation data of Chengdu Shuangliu International Airport. Compared with the fast recognition method, the correct recognition rate of this method is increased by 2.8%, the false negative rate is decreased by 2.7%, and the false alarm rate is decreased by 20.86% (Fig.18). When the wake vortex interaction is strong, the consistency between the circulation correction method and the B-H model is stronger than that of the fast identification method, which is closer to the calculation results of the circulation theory (Fig.15). At the same time, this paper verifies that the introduction of near vortex core data will increase the correction error in the B-H model fitting process (Fig.16).It has been verified that the optimization method proposed in this paper has significantly improved the recognition accuracy under near-ground conditions. Due to the complexity of the real atmosphere and terrain environment of the airport, the method still has a small amount of false identification, false alarm and missing report (Tab.3). The circulation correction method proposed in this paper mainly focuses on the circulation calculation problem caused by the stretching or compression of the wake vortex. In the future, it is necessary to consider the circulation deviation caused by system factors such as measurement resolution and volume averaging effect. NASA is also planning to quantify these errors to improve the ability of lidar to measure wake vortex intensity. Continuing to optimize the performance of the algorithm will be conducive to the application of lidar in airport security, aircraft design, air refueling, ship landing and other scenarios, and provide reliable data support for the realization of intelligent aviation.