In the process of urbanization, urban risks have become increasingly complex. The monitoring of urban coupled-risk and the situational awareness face challenges such as scattered data, uneven data quality, and difficulties in correlation analysis. The urban coupled-risk monitoring and situation-awareness technology based on large language models were focused. The Baichuan2-7B model was selected and optimized through fine-tuning and human-feedback-based reinforcement learning (COOL RLHF). The experiment employed datasets such as COIG, BELLE 1.5M and other multi-source data, which were trained after being preprocessed. The results show that the accuracy rate for urban planning and construction risk judgment reaches 74.84%, while the accuracy rate for public opinion attitude judgment is 70.19%. Various urban planning risks are successfully monitored, public opinion hotspots are captured, and emotional tendencies are analyzed. The accuracy rate for predicting the risk development trend is 83.25%, and the matching degree between situational assessment and reality is 85.07%, receiving high recognition from professionals. The research results provide effective tools and references for urban safety management.