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Street tree continuous spray methods cause serious environmental pollution, however, the existing target spray technologies for tree are difficult to extend to complex urban environment. Aiming at the above problems, recognition method of street tree target was studied, which obtained the information of crown position and distance in real time and provided an accurate spraying basis for street tree toward-target spraying. The research results would improve the intelligent level of medical equipment for prevention and control of street tree and provide theoretical and technical support for street tree pest control, which had low injection, fine spraying, less pollution and high efficiency. Vehicle-borne 2D LiDAR was used to capture 3D point cloud data of street, and variable-scale grid index of point cloud was constructed to process point cloud data online and search neighborhood fast. Height, depth, density and covariance matrix features were extracted from spherical neighborhood of point cloud data, and an 11-dimensional feature vector was constructed. Distribution characteristics of features were analyzed and support vector machine algorithm based on the radial basis kernel function was used to fuse features and learn a point cloud classifier of crown. FIFO buffer was used to save point cloud frame sequences, and then street tree target can be recognized on-line. The classification error rate on the test set was less than 0.8%, with a detection rate more than 99.4% and a false alarm rate less than 0.9%. Four most discriminative features were selected, which were height mean, depth mean, height range and height variance.