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Construction hazards occur at any time in outfield test sites and frequently result from improper interactions between objects. The majority of casualties might be avoided by following on-site regulations. However, workers may be unable to comply with the safety regulations fully because of stress, fatigue, or negligence. The development of deep-learning-based computer vision and on-site video surveillance facilitates safety inspections, but automatic hazard identification is often limited due to the semantic gap. This paper proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. A BERT-based information extraction model is presented to automatically extract the key regulatory information from outfield work safety requirements. Subsequently, an on-site scene parsing model is introduced for detecting interaction between objects in images. An automatic safety checking approach is also established to perform PPE compliance checks by integrating detected textual and visual relational information. Experimental results show that our proposed method achieves strong performance in various metrics on self-built and widely used public datasets. The proposed method can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification.