Abstract: 【Objective】The neural network model was used for stand height prediction. Different types of forest survey factors and neural network training algorithm were examined. The results were expected to provide new method for stand height prediction.【Method】Stand mean height prediction model was developed based on the 314 observations from 168 permanent sample plots of Larix olgensis plantation in Jilin Province with BP neural network modeling technology. Firstly we input the age, then input the site factor and competitive factor to analyze their contributions to stand mean height. We applied Bayesian-regularization algorithm and Levenberg-Marquatdt algorithm for network training based on tansig function and purelin function in Matlab R2016b as neural transferring function. We compared and analyzed the differences of the two algorithms as training functions. 【Result】The analysis result showed that no matter which training algorithm was adopted, the estimation precision of stand mean height would be improved after the age, site factor and forest competitive factor were added in succession and Bayesian-regularization algorithm had better generalization ability than L-M training algorithm. The coefficient of determination R² was 0.521 0, RMSE was 2.091 7, MAE was 1.627 6 when Bayesian-regularization algorithm was adopted and age as input factor; when site factor was added subsequently, R², RMSE and MAE was 0.573 6(increased by 10.10%), 1.973 6(decreased by 5.65%), and 1.579 7(decreased by 2.94%), respectively; when forest competitive factor was added, R², RMSE and MAE was 0.845 5(increased by 47.40%), 1.187 9(decreased by 39.81%), and 0.968 5(decreased by 38.69%), respectively.【Conclusion】Bayesian regularization neural network modeling could be accurate for stand mean height prediction of Larix olgensis plantation. The precision was improved after site and forest competitive factor were taken into consideration and forest competitive factor gave more contribution than site factor.