FAR-AM: A hybrid attention framework for fire cause classification

Automated cause classification of fire accident reports (FIREAR) is crucial for enhancing public safety and developing data-driven prevention strategies. However, existing deep learning models often struggle with the unique challenges these documents present—namely their extreme length, high semantic noise, and fragmented causal information. To overcome these limitations, we propose the Fire Accident Reports Attention Mechanism (FAR-AM), a novel hybrid deep learning framework. FAR-AM first uses a large language model (LLM) to preprocess lengthy raw reports into concise, high-signal summaries. Its core architecture then employs an inter-layer self-attention mechanism to dynamically fuse hierarchical features across all encoder layers of BERT. The fused features are subsequently processed by a TextCNN for final classification. We evaluate FAR-AM on AGNews(title), AGNews(content), THUCNews, and our real-world FIREAR corpus. FAR-AM outperforms strong transformer baselines, including RoBERTa. On the FIREAR dataset, it achieves 73.58% accuracy and 70.65% F1. A comprehensive ablation study further validates the contribution of each component in the multi-stage framework. These results indicate that, for complex domain-specific tasks, specialized hybrid architectures can be more effective and robust than monolithic, general-purpose models.