The point cloud distribution data were used for the first time to extract the characteristic parameters of tree trunks via multi-station scanning. The established hierarchical feature-based volume model will provide a reference for utilizing terrestrial laser scanning (TLS) to monitor dynamic changes in forest resources. Background and Objectives: The rapid development of active remote sensing laser scanning technology has led to the accumulation of substantial data on long-term forest changes. However, questions remain about how these data can be used to facilitate the accurate analysis of forest dynamics and which parameters can be used to reflect the high-precision information provided by point cloud data. Materials and Methods: This study collected three phases of data over 5 years from an artificial Liriodendron chinense forest. A series of the height-related characteristic parameters was extracted from the scanned points of each tree stem, which includes a novel feature of the height cumulative percentage (Hz%) proposed by us. Meanwhile, taking the manually measurements directly from the terrestrial laser scanning (TLS) data as the ground truth, the performance of various models combined with the characteristic parameters on the prediction of the wood volume of each tree was evaluated for the purpose of determination of the optimal parameters and prediction models suitable for the tree species of Liriodendron chinense. Results: The upper diameter accuracy obtained by multi-station scanning is high, and the correlation coefficient with manually measured data is 0.9864. The shape of the upper tree trunk extracted by the point cloud is equivalent to that of the analytical tree with inflection points at 25% and 50% of the height. Among the correlations between the hierarchical features and volumes, the parameters with the highest correlations are H25 and H50. The hierarchical parameters were selected for volume modeling. H25 and diameter at brest heigh (DBH) were used for all three phases, for which the fitted R² reached 0.951, 0.957 and 0.901. The modeled dynamic volume change was highly correlated with the actual point cloud-extracted volume change. In the linear relation, the intercept is -0.081, and the slope is 1.14. Conclusions: The H_z% value provided by multi-station scanning was closely related to the characteristic stumpage parameters and could be used to invert the dynamic forest structure. The volume model based on point cloud hierarchical parameters could be used to monitor the dynamic changes in forest volume and provide an updatted reference for applying TLS point clouds for the dynamic monitoring of forest growth information.