In order to improve the optimization efficiency of light gradient boosting machine (LightGBM) hyper-parameters, and obtain the global optimal model, we propose a parallel optimization method for LightGBM hyper-parameters based on message queuing mode. According to the pre-selection range of hyper-parameters, each set of hyper-parameters is sent to the queue in message mode. Every node trains the model with the message obtained from the queue in parallel mode and verifies the accuracy rate of the model. Finally, the model with the highest accuracy is selected to calculate the dataset to be predicted. Experimental results show that compared with the traditional grid search, Bayesian optimization, random search method and message queuing serial optimization, the proposed method has the highest speed and largest area under curve (AUC ) value.