文章融合大语言模型（LLMs）与深度学习，面向领域知识图谱构建全流程提出了一种LLMDL框架：借鉴文本分类思想开展数据预处理；利用LLMs半自动化构建领域本体；对W2NER命名实体识别模型开展领域自适应优化，基于LLMs完成数据标注及结果验证；考虑不同关系间相关性开展关系抽取；融合SBERT与LLMs实现实体对齐；构建高质量DKG并面向特定场景予以应用。实验结果表明：所提出的方法在兼顾文本价值的同时可有效控制文本长度；命名实体识别较原模型F1值提升2.24%；关系抽取中F1值较传统BERT模型提升22.07%；知识融合中84.85%的实体实现规范表达。

This paper integrates Large Language Models (LLMs) with deep learning to propose an LLMDL framework for the entire Domain Knowledge Graph (DKG) construction process. The framework includes: data preprocessing drawing on text classification principles; semi-automated domain ontology construction via LLMs; domain-adaptive optimization of the W2NER named entity recognition (NER) model with LLM-based data annotation and result verification; relation extraction considering inter-relation correlations; entity alignment through the integration of SBERT and LLMs; and construction of high-quality DKG with scenario-specific applications. Experimental results show that the proposed method effectively balances text value while controlling text length; improves the F1 score of named entity recognition by 2.24% compared with the original model; achieves a 22.07% F1 score improvement in relation extraction compared with traditional BERT models; and 84.85% of entities achieve standardized expressions in knowledge fusion.