Airborne LiDAR(light detection and ranging) is a reliable method for high-precision inversion of individual tree parameters, but estimating individual tree biomass has always been a difficult and hot research topic. Taking pinus massoniana and Eucalyptus as research objects, based on the extraction of tree height and crown width from airborne LiDAR data as independent variables, assisted by morphological characteristic factors such as CSR(crown shape ratio) and CR(crown rate), biomass estimation models were constructed using RF(random forest), LS-SVM (least squares support vector machine) and GBRT(gradient lift regression tree) machine learning, and the accuracy of each model was compared and analyzed. The results indicate that adding crown shape feature factors can effectively improve the accuracy of biomass models. Among the three models, the RF model had the best performance. The fitting result R² of the model without the addition of crown shape feature factors is 0.77, and the rRMSE is 21.57%. After the addition of crown shape feature factors, under different combinations, the fitting R² of the model is 0.86, 0.85, 0.85, and the rRMSE was 20.93 %, 20.17 %, and 21.19%, respectively.