Abstract: With the rapid development of three-dimensional point cloud acquisition tools such as Light Detection And Ranging (LiDAR), the semantic information of point clouds has become more and more important in computer vision, intelligent driving, remote sensing mapping and smart cities. Aiming at the limitations of the point cloud semantic segmentation method based on segmented block feature matching, such as cannot handle the over-segmentation and under-segmentation, the semantic segmentation accuracy of street trees and rods is low, a point cloud semantic segmentation method of street trees and rods based on the segmented blocks merging strategy was proposed, which could merge interested segmented blocks after density-based spatial clustering of applications with noise (DBSCAN) clustering segmentation, and the merged objects were classified by calculating their multi-dimensional geometric features, then the semantic segmentation results were optimized by the interpolation optimization algorithm, and finally the semantic segmentation of street trees and rods in the urban road environment was realized. The experimental results show that the method proposed can improve the recall rate and semantic segmentation accuracy of point cloud data such as street trees and rods in an urban road environment to more than 89.9%. The semantic segmentation method based on segmentation merging can well solve the problem of low accuracy of semantic segmentation of street trees and rods under urban roads. This method is of great significance for the research of three-dimensional scene perception and other problems.