The detection of street trees is crucial to the study of the urban ecological environment because they are one of the key components of an urban green space landscape. Given the low applicability and accuracy of the mobile laser scanning point cloud trunk detection method, a method based on multilayer aggregation for identifying the trunk of the street tree point cloud is proposed. On the basis of point cloud filtering and density clustering preprocessing, this method filters point clouds of street trees using multi-feature constraint method according to the variation between urban street trees and other typical features. Then, the bottom-up multilayer clustering and cluster aggregation method is applied to complete the detection and extraction of the trunk point cloud. In this experiment, two groups of roadside tree point clouds with different complexities are carried out to validate the method. The results demonstrate that the proposed method can efficiently complete the extraction of roadside tree trunks with various scene complexities. The accuracy, recall, and F-measure of the extraction results are 93.1%, 94.4%, and 93.7%, respectively. In conclusion, the proposed method can be used to detect the trunk of roadside trees with large density differences and incomplete point clouds.