With the rapid advancement of artificial intelligence technology， large language models （LLMs） are increasingly being applied across various domains. However， the lack of high-quality， manually curated question-answering datasets in the field of aero-engine has hindered the practical application of expert-level question-answering model. To address this issue， this paper proposes an automated method for constructing question-answering datasets based on LLMs， which generates high-quality open-domain question-answering data without human intervention. During the data generation phase， the method employs in-context learning and input-priority generation strategies to enhance the stability of the generated data. In the data filtering phase， a dual evaluation mechanism is established， combining faithfulness assessment based on source text similarity and semantic quality evaluation using large language models， to automatically filter out hallucinated or anomalous data and ensure factual reliability. Experimental results demonstrate that the proposed method significantly improves the quality of the generated dataset. Models fine-tuned on this dataset exhibit notable performance improvements in aero-engine domain knowledge question-answering tasks. The findings of this study not only provide a solid foundation for the application of large language model in the aero-engine domain but also offer valuable insights for automated dataset construction in other complex engineering fields.