Abstract: Twenty quadrats( 30 m × 30 m ) were set up in Eucalyptus grandis × E. urophylla plantation in Gaofeng Forest Farm of Guangxi. Each quadrat was divided into 9 plots( 10 m × 10 m ), and 4 plots constituted a medium plot( 20 m × 20 m ). Parametric models and machine Learning algorithms were used to estimate stand volume of E. grandis × E. urophylla plantation with mean height and stand density. Result shows that stand density indicators, including number of per hectare, stand density, were positively correlated with the stand volume of E. grandis × E. urophylla plantation. Among all stand volume models of E. grandis × E. urophylla plantation, the variable parameter model(R² = 0. 9973, RMSE = 4. 64 m³/hm²) based on stand density and average height was better than the RF model(R² = 0. 9617, RMSE = 18. 53 m³/hm²) based on number of per hectare and average height; 3 sets of standard residuals of test samples of stand volume estimation model all fell in the −2, 2 strip region, indicating that the 2 models had good adaptability in different area scales(100, 400 m² and 900 m²). The correlation between the models and the stand volume of E. grandis × E. urophylla plantation were well(R² = 0. 9200 – 0. 9973). Root Mean Square Error(RMSE) were well(RMSE = 4. 64 – 25. 16 m³/hm²). Compared with number of per hectare, stand density had a better ability to explain changes in stand volume. The stand volume estimation models, based on stand density and average height in the scale of 100 m² plots, were well adapted to the medium and big sample plots(400 m² and 900 m²).