## 5 Conclusions  
An intelligent recognition algorithm for discovery, extraction, and characterization of earthquake information in media and Internet texts that appear in the hours and days following significant seismic events was proposed and tested. It is based on a BERT-BiLSTM model and is a significant improvement over previous methods for examination of text data with specific content and incremental changes, such as earthquake information text. The effort required collecting earthquake-related data, building a historical earthquake emergency dataset, labeling it, and extracting sentence semantic information through a BERT pretraining model. In use, the BERT is combined with BiLSTM to obtain the contextual semantic environment of the sentence. An attention optimization layer is added to extract word weights, and ultimately, the earthquake news text is recognized by a CRF algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Release Time** & **Earthquake Emergency Information** & **Extracted Entities** \\ \hline
5 September 2022, 1:28 p.m. & (The Sichuan Provincial Emergency & \\  & Rescue Team dispatched 3 rescue & VOL: 14 \\  & command vehicles, and a total of & \\  & 14 people were rushed to Luding County, & \\  & Ganzi Prefecture, the epicenter.) & \\ \hline
5 September 2022, 1:40 p.m. & (Seven rescue teams from Ganzi, & \\  & Chengdu, Deyang, Leshan, Ya\(\tilde{\text{an}}\), & \\  & Meishan, and Ziyang, with a total of & \\  & 530 earthquake rescue forces, rushed to & \\  & the epicenter to start rescue operations. & \\  & At present, 635 rescue forces, including & \\  & armed police, firefighters, medical & \\  & treatment, communication and electricity, & \\  & and traffic security are carrying out & \\  & rescue work. Three hundred rescuers & \\  & have arrived at the epicenter, a rescue & \\  & channel has been rushed through, drones & \\  & have been sent to investigate, and some & \\  & houses have been damaged.) & \\ \hline
9 & 5 & 122\(\uparrow\)52\(\uparrow\) \\
5 September 2022, 3:50 p.m. & (At 12:52 & \\  & p.m. on September 5, an earthquake with & TIM: 12:52 on September 5; PLA: near \\  & a magnitude of 6.8 occurred near Moxi & \\  & Town, Luding County, Ganzi Prefecture, & \\  & Sichuan Province, with a focal depth of & \\  & 16 km. The earthquake has killed seven & \\  & people. Damage to roads, & \\  & communications, houses, etc. is being & \\  & checked.) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._After collecting earthquake emergency texts for 48 h after three earthquakes (namely, the 6.1 magnitude earthquake in Lushan County, Ya'an, Sichuan on 1 June 2022, the 6.0 magnitude earthquake in Markang City, Aba Prefecture, Sichuan on 10 June 2022, and the 6.8 magnitude earthquake in Luding County, Ganzi Prefecture, Sichuan on 5 September 2022) and using the model proposed in this ontology for intelligent text recognition, the experimental results show that the method in this paper had good performance in the recognition of incrementally changing earthquake information text. It can achieve effective recognition of the named entities of earthquakes, extract key information such as rescue vehicles and personnel in their first occurrence in the mass of information on earthquakes, and improve the processing ability of massive earthquake news text information flow during an earthquake emergency. The trustworthy high accuracy and effective performance reduces the manual processing otherwise required to extract the text information, speeding up the application of this auxiliary information to earthquake emergency rescue and relief decision making and providing orderly followability. This model can be used to discover the value of earthquake news. However, there are some shortcomings to this model.