Abstract: Based on data from 700 sample plots of Cunninghamia lanceolata plantations in the northwestern area of Fujian Province, consisting of middle-aged, near-mature and mature trees, we established a yield model with stand age, site index, stand density and average diameter at breast height (DBH) as input variables and stand volume as the output variable. We used two machine learning methods, i.e., a back propagation (BP) neural network and a support vector machine (SVM). The parameters used in both modeling methods were optimized by a genetic algorithm. We randomly divided the plots into two halfs, i.e., a 350 plot training set and a 350 plot test set and compared the fit and prediction accuracies of both models. After parameter optimization, the accuracies in fitting both models were 0.935 37 for BP and 0.936 33 for the SVM, with prediction accuracies of 0.921 30 for BP and 0.926 97 for the SVM. The average relative errors of both the training and test sets were less than 7% for both models. We conclude that both models established a basis for simulating and predicting stand yield of C. lanceolata plantations, given that the accuracy of both models was quite high with good performance of prediction. In order to analyze the differences in the results of both models, the 350 plot test set was evenly divided into seven groups. We calculated the prediction accuracy in each group with two optimization models and used a t-test to compare the absolute difference between the prediction and fitting accuracy. The results showed no significant difference between the two methods. All the same, the slight improvement in precision is important and valuable for monitoring forest resources as well as for predicting the dynamics of stand growth. We found that the accuracy of the fit and generalization ability of SVM were better than those of BP. Therefore, we recommend the SVM for providing new insights for research in forest yield models.