To address the limitations of traditional single-tree Diameter at Breast Height （DBH） measurement methods， including inefficiency， restricted accuracy， and systematic underestimation of parameters due to point cloud data incompleteness， this study proposes an optimized approach integrating hierarchical slicing strategies with multi-model collaboration to enhance the precision and applicability of 3D laser point cloud technology in DBH extraction. A multi-thickness hierarchical slicing framework was designed for the critical DBH measurement interval （1.2~1.4 m）， coupled with a comparative analysis system incorporating least squares， Hough transform， and RANSAC circle-fitting models， validated through field-measured data. Experimental results demonstrated that the hierarchical strategy reduced the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） of full-scene DBH extraction by 18.7% and 22.3%， respectively. In complex scenarios， the RANSAC model achieved the highest coefficient of determination （R²） of 0.980 5， representing a 14.6% improvement over conventional methods， while the Hough transform and least squares methods attained R² values of 0.779 1 and 0.969 1， respectively. The study confirms that hierarchical slicing mitigates fitting inaccuracies caused by data incompleteness through optimized point cloud density distribution， with the RANSAC model exhibiting superior robustness for irregular point cloud fitting. This methodology provides a reliable technical solution for dynamic forest resource monitoring and carbon sequestration quantification， advancing the application of 3D laser scanning in smart forestry management.