| A random forest optimization method includes driving a processor to generate a setting value group according to a Simplification Swarm Optimization rule; driving the processor to transform the setting value group into a plurality of decision tree codes and a plurality of weight values corresponding to the decision tree codes of a random forest model, the decision tree codes correspond to a plurality of binary decision tree models; driving the processor to calculate an accuracy and a decision tree number corresponding to the setting value group; driving the processor to update a best accuracy and a lowest decision tree number in a database according to the accuracy and the decision tree number of the setting value group; driving the processor to repeating the above steps until a number of a plurality of the setting value groups being equal to a predetermined value. |