The physical processes occurring in the atmospheric boundary layer are important factors in the formation of large scale weather and climate. Therefore, detecting the refined structure of the boundary layer helps to understand the physical evolution characteristics of the atmospheric boundary layer, and the impact mechanism of complex boundary layer structures on atmospheric pollution processes. In order to study the mechanism of air pollution, air pollution forecasting is carried out, ensure ground traffic and aviation safety. It is necessary to establish a reasonable mathematical model to study the laws of changes in the Earth's climate environment system and make predictions, and to have a very deep understanding of the atmospheric boundary layer. At present, fine observation within the boundary layer is mainly obtained through in-situ measurement methods, using wavelet analysis methods to obtain corresponding coherent structures and scale features from in-situ measurement data. The scale characteristics within the atmospheric boundary layer vary with altitude, therefore it is necessary to use two-dimensional measurement data to study the spatial distribution of scale characteristics in the atmospheric boundary layer. To overcome the shortcomings of existing methods, we propose a Shearlets wavelet multi-scale method. The traditional method uses gradient method and wavelet covariance transform to extract the height of the boundary layer, but due to the influence of noise and the structure of the aerosol layer, this method is prone to significant experimental errors. This article proposes the use of wavelet multi-scale analysis to refine the boundary layer feature structure, in order to screen out effective detail information and improve the accuracy of exploring the height of the boundary layer. To begin with, we calculate the preprocessed distance corrected squared signal (PRR) single profile with the synthesized wavelet, and then perform scale transformation through matrix Mj. Last but not least, the scale factor a=1-128, where the height corresponding to the maximum value of the time scale energy spectrum is the height of the atmospheric boundary layer.From Fig.4, it can be concluded that the height of the boundary layer can be inverted using the gradient method, wavelet covariance method, and wavelet multi-scale method. Firstly, due to the fact that the boundary layer does not undergo sudden changes in physics in a short period of time, compared with the poor discontinuity of the gradient method and wavelet covariance method, the wavelet multi-scale algorithm has higher stability and continuity. Secondly, in the presence of clouds, it is obviously unreasonable for gradient and wavelet covariance methods to identify cloud base misjudgments as boundary layer heights. Wavelet multi-scale algorithms have higher accuracy and a lower probability of misjudgments. The scatter plot shows a significant correlation between the determination of boundary layer height using wavelet covariance transform and the determination of boundary layer height using wavelet multi-scale, as well as the determination of boundary layer height using gradient method and the determination of boundary layer height using wavelet multi-scale under static time. We conduct data analysis on special time points, identify the main factors that affect the height of the boundary layer, and determine the atmospheric boundary layer height values for each time period. The main focus of this work is to use GBQ L-01 lidar to refine two-dimensional observations of time and space within the boundary layer. Aerosols are used as tracers to analyze information on the spatial height of the boundary layer. Shearlet wavelet is used as a tool to perform multi-scale analysis on the obtained lidar data and extract information at different scales. In future work, by observing other data through lidar, we will continue to analyze the temporal and spatial distribution characteristics of aerosol particles and extract more structural features.