“In the field of Chinese named entity recognition (NER) for the process industry, a new study introduces significant research progress. Experts have developed DDiNER, a domain dictionary-guided NER framework that integrates a hierarchical industrial domain dictionary with BERT via a hierarchical lexicon adapter, combined with BiLSTM and CRF layers. This innovative approach addresses challenges like ambiguous entity boundaries and limited annotated data, achieving superior performance with average precision, recall, and F1-scores of 95.75%, 95.73%, and 95.74%, respectively. The study provides an effective and scalable solution for industrial Chinese NER, paving the way for advanced intelligent applications.”