The invention discloses a method for optimizing random forest parameters for machine learning model training. The method comprises the steps that firstly, dividing the whole ant population into a plurality of sub-populations; Then, allowing each sub-population to correspond to one partition in the RDD, and designating independent evolution in one partition; And finally, exchanging information among the sub-populations by utilizing a migration operator. Compared with the traditional grid search, the parallel ant lion algorithm based on the Spark can efficiently find out a better parameter combination to improve the classification precision of the random forest, and under a big data distributed Spark platform, the optimization calculation speed is high, the acceleration effect is obvious, and the parallel ant lion algorithm based on the Spark can be used as a next generation parameter optimizer of a cloud computing platform.