This study aims to improve the prediction accuracy of rockburst intensity grades in gently dipping rock strata, and provide reliable technical support for risk prevention, long-term stable production and sustainable development in underground engineering construction. Therefore, a rockburst intensity grade prediction model combining multi-strategy improved particle swarm optimization (MIPSO) with random forest (RF) is proposed, and the stress coefficient (SCF), brittleness coefficient (B) and elastic energy index (W_et) are selected as input indicators. After the algorithm and model are validated using benchmark test functions and the five-fold cross-validation method, their performance is compared with that of the other four models based on evaluation metrics, and the Shapley interpretability analysis (SHAP) is conducted. The results show that the performance of the model is superior to that of other models, and the importance ranking of the prediction indicators is SCF, W_et, and B. Finally, the application software developed based on the model is used for rockburst intensity grade prediction; rockburst prediction indicators are obtained through experiments and numerical simulations, and the prediction results obtained after importing them into the software are consistent with the actual situation, which proves that the rockburst prediction framework constructed in this paper has practicality.