Abstract: Industrial enterprise safety management is a core component of ensuring production safety. However, traditional methods suffer from inefficiencies and insufficient accuracy in knowledge acquisition, sharing, and application. This study designs and implements an AI large model-based question-answering system for industrial safety management knowledge. By constructing a multi-dimensional knowledge graph, optimizing large model training strategies, designing a hierarchical system architecture, and integrating Retrieval-Augmented Generation (RAG) technology, the system significantly enhances the intelligent application level of safety knowledge. Practical application cases demonstrate that the system can rapidly respond to complex queries, improve employee safety awareness, and effectively reduce accident rates. Additionally, addressing limitations such as inadequate complex problem-solving capabilities of models and data quality issues, this study proposes improvement directions including dynamic knowledge updating and multimodal interaction optimization, providing theoretical and practical references for the intelligent transformation of industrial safety management.