Abstract: Accurate Chinese named entity recognition (NER) in the process industry is crucial for applications such as information extraction, knowledge graph construction, and intelligent decision-making. However, challenges, including ambiguous entity boundaries, semantic overlaps, and limited annotated data, significantly hinder performance. To address these issues, this study proposes DDiNER, a domain dictionary-guided Chinese NER framework that integrates a hierarchical industrial domain dictionary with bidirectional encoder representations from Transformers (BERT) via a hierarchical lexicon adapter (HLA), combined with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) layers for multilevel feature fusion. Experimental results show that DDiNER 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 confirms its robustness and strong capability in recognizing unseen and long-tail entities. This study provides an effective and scalable solution for industrial Chinese NER, with significant potential for downstream intelligent applications.

Key words: Named entity recognition (NER); Process industry; Domain dictionary; Hierarchical lexicon adapter (HLA)

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