Frequent accidents in the construction sector arise from the dynamic coupling of multiple risk factors, while conventional single-factor approaches fail to capture the underlying complexity. Drawing on 702 accident investigation reports, this study develops an intelligent, data-driven framework that integrates large language model–based risk identification with association rule mining to systematically uncover risk factors and their coupling patterns. A DeepSeek-based model is employed to extract risk factors from unstructured text, followed by cosine similarity–based optimization to refine factor representations. The FP-Growth algorithm is then applied to identify strong association rules among risk factors. The results reveal that deficiencies in the management dimension account for 68.30% of all identified risks, with inadequate safety education and training emerging as the central hub in the risk coupling network, which is further corroborated by complex network analysis. Moreover, a cascading transmission pathway is identified, whereby environmental deficiencies induce weakened safety awareness, which in turn leads to unsafe behaviors. These findings further demonstrate the nonlinear amplification effects arising from concurrent management failures. By establishing a transformation pathway from unstructured textual data to structured risk knowledge, this study provides a robust, data-driven foundation for precise risk identification and systematic prevention in construction safety management.

Prefabricated construction represents an advanced approach to sustainable development, and safety issues in prefabricated construction projects have drawn widespread attention. Safety accident case reports contain a wealth of safety knowledge, and extracting and learning from such historical reports can significantly enhance safety management capabilities. However, these texts are often semantically complex and lengthy, posing challenges for traditional Information Extraction (IE) methods. This study focuses on the challenge of Named Entity Recognition (NER) in long texts under complex engineering contexts and proposes a novel model that integrates Modern Bidirectional Encoder Representations from Transformers (ModernBERT),Bidirectional Long Short-Term Memory (BiLSTM), andConditional Random Field (CRF). A comparative analysis with current mainstream methods is conducted. The results show that the proposed model achieves an F1 score of 0.6234, outperforming mainstream baseline methods. Notably, it attains F1 scores of 0.95 and 0.92 for the critical entity categories “Consequence” and “Type,” respectively. The model maintains stable performance even under semantic noise interference, demonstrating strong robustness in processing unstructured and highly heterogeneous engineering texts. Compared with existing long-text NER models, the proposed method exhibits superior semantic parsing ability in engineering contexts. This study enhances information extraction methods and provides solid technical support for constructing safety knowledge graphs in prefabricated construction, thereby advancing the level of intelligence in the construction industry.