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• Acceleration of solving a forest stand selection problem using neural networks. • Iterative identification of optimal harvesting schedules has large computing cost. • Direct method reduces computing cost by relaxing the curse of dimensionality. • Indirect method tries further acceleration by root-finding using neural networks. • Direct method was viable in an application and expected to accerelate 8.7 times. A method of forest stand selection for subsidization requires iterative identification of optimal harvesting schedules for each stand. The time required for the iterative calculation and the existence of numerous stands in the focused regions prevent the application of the stand selection method in practice. In this study, we developed two methods to reduce the calculation cost of stand selection using neural networks. One method approximates the necessary indices for stand selection using neural networks, thereby reducing the calculation cost. The other method reduces the calculation cost during the iterative optimization processes and indirectly predicts the necessary indices. Acceptable predictions of the necessary indices were made by the first method even with imbalanced training data. Although neural networks with reasonable accuracy were generated using the second method, the predicted indices had unignorable errors owing to indirect predictions. The first method generated neural networks with reasonable accuracy using 20,000 tuning and validation data, which reduced the computation time from 61.09 h to 7.62 h when applied in Nagano Prefecture, Japan. [ABSTRACT FROM AUTHOR]