Owing to their unstructured format, construction safety standards struggle to meet the demands of modern construction safety management. Although large language models (LLMs) possess strong natural language processing capabilities, their use in specialized fields such as construction safety is hindered by the problem of “hallucination”. Combining graph retrieval-augmented generation (GraphRAG) with LLMs, an intelligent information retrieval and question answering system based on the augmented large language model for construction safety was constructed. A knowledge graph of construction safety standards was first built to achieve a structured representation of standard texts. Subsequently, a deep learning-based approach was leveraged to conduct named entity recognition and matching of query statements, facilitating entity-level semantic comprehension and knowledge localization. By integrating prompt engineering with LLM interaction, the accuracy of model responses was effectively improved. Trial runs of the system confirm its effectiveness across diverse construction scenarios. The findings suggest that this system contributes to elevating the informatization of construction safety management and advances the intelligent utilization of construction safety standard knowledge.