• DDiNER integrates a hierarchical industrial domain dictionary with BERT, BiLSTM, and CRF for multilevel feature fusion, effectively addressing ambiguous entity boundaries and semantic overlaps in complex industrial Chinese text.
• The framework achieves superior performance with average precision, recall, and F1-scores of 95.75%, 95.73%, and 95.74%, respectively, outperforming state-of-the-art models.
• Validation on an independent dataset demonstrates strong robustness and capability in recognizing unseen and long-tail entities.
• DDiNER provides an effective and scalable solution for industrial Chinese NER, with significant potential for information extraction, knowledge graph construction, and intelligent decision-making.