摘要： 以电力领域为例对术语识别任务进行了研究，旨在解决电力行业在数字化转型过程中面临的术语识别挑战。电力行业面临着数据孤岛和知识难以活化利用的问题，要求有更高效的方法将文档中的术语实体转化为可操作的知识以支持决策制定和技术创新。为了应对专业术语难以辨认、新颖术语难以发现等问题，提出了一种基于动态领域图谱与大小模型协同的术语识别方法，从候选术语提取和术语筛选分类两个任务阶段中分别提高术语自动识别的查全率和查准率。首先使用已有术语库构建初代知识图谱，然后查询目标文本相关的节点并结合术语特征进行模型过滤，利用检索增强提示辅助大语言模型提取候选术语，再通过对抗训练获得术语分类的深度学习模型，根据深度学习模型的分类结果迭代动态术语知识图谱。实验结果显示，方法的准确率、召回率和F1值在迭代过程中逐步提升，最终达到了0.8647、0.8565和0.8542，与其他术语识别方法相比，在上述三者指标上均显示出优越性。

Abstract: This study explores automatic term recognition in the electric power domain, addressing challenges faced during its digital transformation, such as data silos and knowledge utilization. To improve the identification of specialized and new terms, a dynamic graph-assisted method combining large and small models is proposed. The approach enhances recall and precision through candidate term extraction and term classification. An initial knowledge graph is built using existing term databases. Target text-related nodes are queried and filtered with term features. A retrieval-augmented large language model extracts candidate terms, followed by adversarial training to develop a deep learning model for term classification. The dynamic term knowledge graph is iteratively updated based on classification results, forming a positive feedback loop. Experimental results show that the method's accuracy, recall, and F1 score improve over iterations, reaching 0.8647, 0.8565, and 0.8542, respectively, demonstrating superior performance compared to other term recognition methods.