Aiming at the low efficiency of hyperparameter optimization of machine learning algorithms and the characteristics of mainstream parameter optimization algorithms, a machine learning parameter optimization method based on parameter parallel mechanism is proposed. This method uses the swarm heuristic algorithm to optimize the parameters of the machine learning algorithm, converts the population into a flexible distributed data set unique to the Spark platform, and calculates the fitness of individuals in the population in parallel. The IC card data of Guangzhou buses are selected as experimental data, random forest and genetic algorithm are used as experimental algorithms, and multiple sets of experiments are designed to verify the proposed learning and training method. The experimental results show that, under the small amount of data, the machine learning parameter optimization method based on the parameter parallel mechanism proposed in this paper can reduce the running time by up to 2 hours and has good scalability compared with the machine learning training method based on the data parallel mechanism.