To address issues related to excessive human influence and prolonged prediction times in rockburst prediction

we propose a rockburst intensity classification prediction model based on automatic machine learning. This model is trained using five automatic machine learning frameworks and evaluated using metrics such as accuracy

precision

recall

and F1-score. Subsequently

we compare the performance of this trained model with results from thirteen common machine learning models. The model developed with the Auto-Sklearn framework achieved a high accuracy of 0.969

while the model created with the Auto-Gluon framework

although having the lowest accuracy among the five frameworks

still achieved an accuracy of 0.927. Rockburst prediction models constructed using AutoML frameworks significantly outperformed traditional machine learning algorithms. The Auto-Sklearn-based model exhibited the highest accuracy. In summary

the optimized model was applied to predict rockburst events at the Shaiqi River phosphate mine

and the predictions were consistent with the actual observations on-site. This indicates that the automatic machine learning-based model for rockburst intensity classification prediction can accurately predict rockburst incidents in real-world engineering settings.