###### Abstract
[24] performed rockburst prediction using seven machine learning methods based on 314 real cases of rockbursts, and the output showed that the integrated tree had superior prediction performance compared to other ML models; [PERSON] [25] and colleagues developed a PSO-ELM rockburst intensity level prediction model, which they validated with 15 examples of rockbursts at the Jiangbian Hydropower Station; the model exhibited superior predictive capabilities, as evidenced by the results. [PERSON] et al. [26] evaluated the learning ability of Boosting and Bagging algorithms based on rockburst instance data and also used cross-validation as a method of adjusting hyper-parameters and after analyses showed that the Bagging method was the most effective for the prediction of the rockburst intensity level. [PERSON] et al. [27] established the Borderline-SMOTE 1-Adaboost model; with this method, to a certain extent, the problem of data imbalance is solved, and the prediction accuracy is higher after comparing with various models, and the model is applied in the forecasting of rockburst intensity in the Qinling water tunnel, which provides a reference for the early warning of rockburst disasters during the construction of deep buried tunnels. [PERSON] et al. [28] proposed the LightGBM-TCN-RF rockburst intensity level prediction model, which makes the model able to predict the danger degree of rockburst characteristic variables, providing a reference for the field of rock explosion prediction and monitoring. All of these research methods mentioned above have achieved some success in rockburst prediction research and have certain advantages for specific problems while enriching the disaster theory related to rockburst-prediction research. However, considering that each algorithm inevitably has some shortcomings in practical engineering applications, such as SVM, KNN and other models, the application of the model tends to be less robust, and the model's predicted performance in different regions, different geological conditions, different climates and other conditions of the differences in the performance of the rockburst prediction of the differences exist, often with the input of the parameters of the different changes. With the progress of machine learning technology, more and more scholars are trying to integrate multiple models [29; 30] to overcome the shortcomings of the poor generalisation ability of a single model and have achieved some results. In the field of rockburst prediction, several scholars have also made attempts to integrate models [31]; however, the generalisability of these models needs further research [32]. In this paper, to address the limitations of the current research methodology and enhance the model's predictive power and generalisability, an NGO-CNN-BiGRU-Attention rockburst prediction model based on intelligent optimisation algorithms and deep learning techniques is proposed, which is trained based on 287 sets of real rockburst data in different regions, under different geological conditions and at different times and is combined with RF, SVM, KNN, ELM, CNN, BiGRU and CNN-BiGRU-Attention models; the findings demonstrate that the NGO-CNN-BiGRU-Attention model exhibits greater prediction accuracy. In the end, the model is validated using eight sets of real rockburst cases in the Daxiangling Tunnel, and the results indicate that the model exhibits robust generalisation capacity and is suitable for engineering applications.