知识图谱技术对汽车高效的故障诊断具有重要的意义，现有汽车故障知识图谱构建存在着实体识别模型效果不佳、无法解决嵌套实体等问题。针对上述问题，通过采用全词掩码的预训练语义模型、加入对抗训练和改进嵌套实体识别模型的方式提高实体识别模型效果，提出了一种改进的嵌套实体识别模型。实验结果表明，所提模型F1值（F₁）、精确率（P）和召回率（R）相比基线模型分别提高了3.56%、4.08%、3.05%，相比其他模型也有不同程度的提高，验证了所提模型对汽车维修领域实体识别具有显著效果。同时，基于构建的汽车故障知识图谱，实现了汽车故障知识智能问答原型系统，展示了知识图谱技术在汽车故障诊断与维护领域的应用前景。

Knowledge graph technology is of great significance to the efficient fault diagnosis of automobiles. The existing construction of the knowledge graph of automobile faults has problems such as a poor entity recognition model and the inability to solve nested entities. In order to solve the above problems, an improved nested entity recognition model was proposed by adopting the pre-training semantic model of whole word mask, adding adversarial training, and improving the nested entity recognition model. The experimental results show that the proposed model is 3.56%, 4.08%, and 3.05% higher than the baseline model in terms of the F1 value (F₁), accuracy (P) and recall (R), and also has different degrees of improvement compared with other models, which verifies that the proposed model has a significant effect on entity recognition in the field of automobile maintenance. At the same time, based on the constructed automobile fault knowledge graph, the intelligent question answering prototype system of automobile fault knowledge is realized, and the application prospect of knowledge graph technology in the field of automobile fault diagnosis and maintenance is shown.