Automatic Text Analysis and Intelligent Warning System for Building Fires Based on BiLSTM-CRF Model

In response to the problem of delayed warning caused by the inability to quickly and accurately analyze and extract effective information from a large amount of text data in emergency response to high-rise building fires, this study proposed a fire text automatic analysis and intelligent warning system based on the BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) model. The system has achieved the function of extracting key information from fire related texts and providing real-time warnings through intelligent processing. Firstly, the system is constructed through text preprocessing, BiLSTM network feature extraction, CRF model sequence annotation, and other steps. The system uses text preprocessing techniques to clean and segment unstructured data such as emergency reports and logs related to fires. Next, the BiLSTM network is used for deep learning and feature extraction of important information such as time, location, and cause of fire in the text. Finally, the extracted features are annotated in sequence using the CRF model to further improve the accuracy of information annotation. Through intelligent analysis of real-time fire text data, the system can quickly generate warning information and push it to relevant departments, greatly improving the speed and accuracy of fire emergency response. The experimental results show that this system outperforms traditional fire warning systems in text information processing and warning accuracy, with a 14.2% increase in warning accuracy. In terms of response speed, from 4.3 seconds in traditional systems to 3.2 seconds in BiLSTM-CRF systems, it can effectively reduce the potential losses caused by fire hazards. A fire text automatic analysis and intelligent warning system based on the BiLSTM-CRF model achieves accurate extraction and rapid warning of fire information through automated processing of unstructured text data. This significantly improves the processing efficiency and accuracy of fire warning systems, providing new technological means for early prevention and rapid response to high-rise building fires.