In order to explore the prediction accuracy of different models on the average diameter at breast height of forest stands,airborne LiDAR point cloud data and ground measured sample plot datawere obtained simultaneously by the Machang working area of Guihua State-owned Forest Farm in Guizhou Province.By extracting point cloud feature variables at the sample plot level,a machine learning model is used toestimate the average diameter at breast height of the sample plot,variance inflation factor analysis and Pearson correlation test are used to select independent variables.The results indicate that:1)Point cloud feature variables show a strong correlation with the average diameter at breast height of the forest stand,such as the average canopy height and height skewness.2)Machine learning models(random forest,support vector machine,nearest neighbor algorithm)outperform multiple linear regression models,with random forest having the best fitting performance.The determination coefficient(R²) for the random forest model is 0.71,withthe root mean square error(RMSE)of 2.50.3)The difference between the predicted and actual average diameter at breast height of four forest types:Cryptomeria forest,mixed coniferous forest,mixed coniferous and broad-leaved forest,and Pinusmassoniana forest further confirms that the random forest model has the highest accuracy and the best fitting effect.In summary,it is feasible to extract point cloud feature variables using airborne LiDAR point cloud data and construct a forest average diameter estimation model based on machine learning algorithms.The accuracy of this method meets the application requirements of forest resource investigation and can be used as a technical means to assist in forestry investigation work.