In the process of digital transformation, enterprises are faced with problems such as insufficient semantic understanding of unstructured data and lack of intelligent decision-making basis in driving mechanisms. This study proposes a method that combines a large language model (LLM) and a knowledge graph. First, a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model is used to perform entity recognition and relationship extraction on multi-source heterogeneous texts, and GPT-4 is used to generate semantically enhanced vector representations; secondly, a two-layer graph neural network (GNN) architecture is designed to fuse the semantic vectors output by LLM with business metadata to construct a dynamic and scalable enterprise knowledge graph; then reinforcement learning is introduced to optimize decision path generation, and the reward function is used to drive the mechanism iteration. In the case of the manufacturing industry, this mechanism reduced the response time for equipment failure scenarios from 7.8 hours to 3.7 hours, the F1 value reached 94.3%, and the compensation for decision errors in the annual digital transformation cost decreased by 45.3%. This method significantly enhances the intelligence level and execution efficiency of the digital transformation driving mechanism by integrating large model semantic understanding with structured knowledge.

翻译：在数字化转型过程中，企业面临非结构化数据语义理解不足、驱动机制缺乏智能决策依据等问题。本研究提出一种融合大语言模型与知识图谱的方法。首先，采用微调BERT模型对多源异构文本进行实体识别与关系抽取，并利用GPT-4生成语义增强的向量表示；其次，设计双层图神经网络架构，将大语言模型输出的语义向量与业务元数据进行融合，构建动态可扩展的企业知识图谱；随后引入强化学习优化决策路径生成，通过奖励函数驱动机制迭代。在制造业案例中，该机制使设备故障场景响应时间从7.8小时降至3.7小时，F1值达94.3%，年度数字化转型成本中决策失误补偿降低45.3%。该方法通过大模型语义理解与结构化知识的融合，显著提升了数字化转型驱动机制的智能化水平与执行效率。