【Objective】Due to the complex structure and features, traditional classification models for tree branches and leaf point clouds typically face several problems, including poor stability, low accuracy, model overfitting, and high computational costs. In this study, we propose an improved CART (classification and regression tree) model for leaf and branch classification based on Quercus glauca individual tree point cloud data from terrestrial laser LiDAR.【Method】First, the feature descriptor was constructed according to the neighborhood points, and the optimal value of the neighborhood search parameter was then determined. The CART model was improved by gradually introducing variables and adjusting the structure of the decision tree. The classification results of the improved CART model were compared with those of the Logistics regression and K-nearest neighbor (KNN) models.【Result】The accuracy of the improved CART model using the test data increased after introducing the feature descriptors as variables, exceeding that of the Logistics regression and KNN model by 13.1% and 13.6%, respectively. Moreover, the improved CART model exhibited higher accuracy, better stability, and marked reduced model size following the improvement. In particular, the model size was reduced by 99.9% compared with before the improvement, while the data training time was only 51.3% of that before the adjustment. The comprehensive evaluation index of the improved CART model was approximately 0.9 on both trunk and leaf data, with the difference between accuracy on train data and test data lower than 0.001, indicating no overfitting.【Conclusion】The improved CART model has a high accuracy and stability, and achieves good classification results on small samples. This study provides a methodological reference for the accurate and rapid classification of trunk and leaf point clouds from terrestrial laser LiDAR.