Modern industrial systems have grown increasingly extensive, complex, and hierarchical, with operations relying on numerous knowledge-based queries. These queries necessitate considerable human resources while also requiring high levels of accuracy, subjectivity, and consistency, all of which critically influence operational efficiency. To overcome these challenges, this study proposes an industrial retrieval-augmented generation (RAG) method designed to enhance large language models (LLMs) using domain-specific knowledge, thereby improving the precision of question answering. A comprehensive industrial knowledge base was constructed from diverse sources, including journal articles, theses, books, and patents. A Text classification model based on bidirectional encoder representations from transformers (BERTs) was trained to accurately classify incoming queries. Furthermore, the general text embedding-dense passage retrieval (GTE-DPR) model was employed to perform word embedding and vector similarity retrieval, facilitating the alignment of query vectors with relevant entries in the knowledge base to obtain initial responses. These initial results were subsequently refined by LLMs to produce accurate final answers. Experimental evaluations confirm the effectiveness of the proposed approach. In particular, when applied to ChatGLM2-6B, the RAG method increased the ROUGE-L score from 32.52% to 55.04% and improved accuracy from 50.52% to 73.92%. Comparable improvements were also observed with LLaMA2-7B, underscoring the RAG framework’s capability to significantly enhance the accuracy and relevance of industrial question-answering (QA) systems.