Forest canopy height and trees distribution pattern are important indicators for studies of characterizing plant community vertical structure, which are also important parameters for calculating forest biomass distribution pattern. The traditional forest community survey methods expend a lot of manpower and resources and are also difficult to measure large-scale community structures easily. It is also difficult to obtain accurate topographic information and vertical structures using general traditional remote sensing images. In recent years, the rapid development of Light Detection and Ranging (LiDAR) technology makes it much easier to extract three-dimensional features of vegetation and is widely used in forest ecosystem detection and simulation. UAV-LiDAR has become a more flexible LiDAR technology which had ability to obtain wide range of vegetation canopy information with the development of low-altitude unmanned aerial photogrammetry and remote sensing technology. Due to the penetrability of the laser and the influence of canopy density on different vegetation types, the application of this technology is usually limited to the studies of measuring vertical structure of the coniferous forest community while is less used in the studies of evergreen broad-leaved forest. In order to explore the feasibility of applying the existing UAV-LiDAR equipment and vertical structure extraction analysis technology to evergreen broad-leaved forest vertical structure study, we used UAV-lidar to calculate canopy height based on Canopy Height Model (CHM) which is obtained from the value of Digital Surface Model (DSM) minus Digital Terrain Model (DTM). We also extracted trees location based on the local maximum value to calculate trees distribution pattern by Clark-Evans nearest neighbor analysis of large tree in three 1 hm² plots of Ailao Mountain evergreen broad-leaved foresy. The results of accuracy analysis show that the accuracy of canopy height's measurement is above 95%. There are very significantly correlations between the vegetation height measured by LiDAR and field measurement. The maximum R² is 0.927 while the minimum is 0.833. The mean values of canopy height of the three samples are 18.79, 19.08, and 17.03 m while the standard deviations are 8.10, 7.34 and 7.17 m, respectively. The average detection percentage of individual tree detection in three samples is 86.3%. The means of average user's accuracy and the producer's precision are 75.69% and 65.15% respectively. The spatial distribution patterns of the whole tall trees in three plots were regarded as aggregated distribution by the result of field measurement, while the result of measurements by LiDAR showed random or uniform distribution. The experiment shows that it is efficient for UAV-LiDAR technology to extract the vegetation canopy height information accurately and obtain the location of trees. However, the accuracy of the tree spatial distribution pattern determining needs to be further improved. Research in future should focus on analyzing of the causes of error caused by LiDAR data from various angles (like environmental factor) and develop more accurate single-wood extraction approaches and vegetation height extraction methods to provide more accurate indicator data for measuring forest biomass and more biological processes by UAV-LiDAR technology.