Abstract: To address the complexity of the stability prediction for high-steep slopes at tunnel portals, an intelligent prediction model based on multi-source information fusion is proposed in this paper. The model integrates three machine-learning algorithms, namely, support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The model builds a dedicated data set of 100 tunnel-portal slopes worldwide. It covers features such as slope height, slope angle, rock-mass strength parameters and geological structures (joints, fractures, faults), enabling intelligent stability assessment. Results show that the random-forest model performs best, with a superior accuracy and recall; feature-importance analysis indicates that the slope angle, pore-water pressure and internal friction angle are the key controlling factors. The SVM model achieves 93 % accuracy but limited generalization, whereas the ANN model reaches 97 % accuracy yet suffers from over-fitting. The study provides reliable decision-support for tunnel-engineering risk prevention and control; future work can further enhance its value through data expansion, model ensemble and real-time monitoring system optimization.

Key words: Slope Stability, Random Forest, Multi-Source Information Fusion, Machine Learning