Deep Mining and Association Recommendation Method for Railway Safety Knowledge Based on Multimodal Information Fusion

The rapid digital and intelligent transformation of railway information systems has created an urgent demand for fine-grained, explainable safety knowledge recommendations. To address the fragmentation of cross-modal associations and insufficient alignment with operational rules exhibited by traditional approaches, a framework integrating multimodal feature fusion with generative reasoning is investigated. A hierarchical railway safety knowledge graph is constructed, and topological features under business constraints are extracted via the Node2Vec algorithm. Simultaneously, a lightweight Transformer encoder (GTE) captured deep semantic embeddings of individual safety clauses. To balance contributions from graph and text features, a tunable weighting strategy is introduced, dynamically controlling the fusion ratio of text vectors and graph embeddings and applying a dual-constraint mechanism based on cosine similarity and predefined rules to generate candidate recommendations. A three-stage progressive retrieval architecture is designed to achieve precise multimodal alignment and suppress noise. Finally, the DeepSeek-R1 large language model served as the reasoning engine, with domain-specific prompting converting retrieved candidates into executable decision plans, thereby enhancing coherence and interpretability. Experiments on 27 safety documents from a railway operator, using a similarity threshold of 0.85 and a maximum of 10 recommendations per query, demonstrated a recommendation accuracy of 95% (an 8-percentage-point improvement over traditional methods) along with significant gains in contextual relevance and explainability. This investigation confirms the synergistic benefits of multimodal retrieval and generative reasoning, providing a robust technical foundation for evolving railway safety knowledge services from precise recommendation to intelligent decision support.