Abstract: Fault Tree Question Answering (FTQA) is a form of intelligent question-answering system built upon the fundamentals of fault tree analysis, where enhancing efficiency and accuracy are key challenges. To address this, a novel method based on large model decision-making has been proposed. By transforming the fault tree into a Directed Acyclic Graph (DAG) and integrating deep learning and natural language processing technologies, this method utilizes large decision-making models to develop an intelligent question-answering system. This system is capable of effectively understanding and responding to various questions related to fault trees, thereby solving multiple critical technical challenges. Experiments conducted on various types and sizes of fault tree models have validated the significant advantage of this system in terms of the accuracy of responses. Research findings demonstrate that the fault tree question-answering method based on large model decision-making not only holds significant theoretical implications but also exhibits substantial practical value in application. This introduces an innovative approach and tool for intelligent fault tree question-answearing, laying a solid foundation for future research and development.

Key words: Fault tree, Intelligent question-answering, Accuracy, Large model decision-making