## 4 Experimental
### Experimental Results  
The recognition performance of the BERT-BiLSTM model proposed in this paper was compared with other legacy models. A total of four sets of experiments were set up to train on the CNN model, LSTM model, BERT-based model, and the BERT-BiLSTM model in this paper. The experimental environment was kept constant, and the \(F1\) value described above was used as the evaluation index. The experimental results are shown in Table 5. The table shows the \(F1\) values of 19 categories of labels under 4 different models, and the last row is the \(F1\) value of model synthesis. Combined with Figures 6 and 7, the horizontal axis lists the categories of key information for the recorded earthquakes. The vertical bars above each are the \(F1\) values predicted for that category. The BERT-BiLSTM model had better recognition accuracy than the other models. In the test sets constructed for 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, the BERT-BiLSTM model proposed in this paper, compared with the traditional CNN model, the LSTM model, and the BERT-based model, improves the results by 18.17%, 10.06%, and 5.5%, respectively. Among them, the BERT model had better performance in contextual semantic extraction and word polysemy resolution in the earthquake news text compared with the CNN model and LSTM model because of its mask setting based on a multi-head attention mechanism, and the BERT-BiLSTM model performed better in the earthquake news text after adding an LSTM layer and attention optimization layer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Value** & **Predicted to Be a Positive** & **Predicted to Be a Counter** \\  & **Example** & **Example** \\ \hline Truth is a positive example & TP & FN \\ Truth is a counter example & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix table. Figure 6: Experimental results for precision, recall, and accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label Name** & **CNN** & **LSTM** & **BERT-Base** & **BERT-BiLSTM** \\ \hline TIM & 71.06\% & 80.58\% & 85.82\% & 90.77\% \\ MAG & 70.55\% & 84.02\% & 85.71\% & 91.01\% \\ PLA & 75.39\% & 84.11\% & 85.63\% & 93.62\% \\ COO & 73.32\% & 82.51\% & 88.52\% & 92.43\% \\ DEP & 79.30\% & 82.55\% & 87.29\% & 93.95\% \\ INT & 77.53\% & 83.01\% & 85.99\% & 93.66\% \\ HUR & 76.29\% & 82.25\% & 85.50\% & 90.31\% \\ DEA & 70.73\% & 81.83\% & 86.83\% & 91.34\% \\ FIR & 70.81\% & 83.93\% & 88.24\% & 93.28\% \\ NET & 72.16\% & 80.66\% & 85.71\% & 90.53\% \\ MAT & 74.48\% & 81.17\% & 89.18\% & 94.09\% \\ GEO & 79.42\% & 82.12\% & 89.65\% & 92.31\% \\ RES & 71.76\% & 80.12\% & 89.32\% & 91.50\% \\ DIS & 79.02\% & 83.51\% & 86.45\% & 93.80\% \\ ECO & 74.15\% & 84.96\% & 86.30\% & 91.42\% \\ VOL & 76.10\% & 81.04\% & 86.40\% & 93.88\% \\ DOC & 71.86\% & 80.92\% & 88.31\% & 93.50\% \\ PEO & 72.90\% & 82.70\% & 86.71\% & 92.65\% \\ TRA & 75.31\% & 84.29\% & 85.43\% & 93.33\% \\  & 74.32\% & 82.43\% & 86.99\% & 92.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results. Finally, tests were conducted on the test sets constructed for the earthquakes listed above. The slices were divided for 48 h after the earthquake, and the data were crawled for each time slice of earthquake news after the three earthquakes. Then, the earthquake attributes in the earthquake news were extracted. Figure 8 shows the number of news items obtained within each time slice after the earthquakes occurred. As can be seen in the graph, the number of news items appearing in the timeline after the earthquakes increased incrementally. The intelligent recognition model of earthquake news text based on the BERT-BiLSTM algorithm in this paper was used for the intelligent recognition of seismic attributes in the incremental seismic emergency information text of three earthquakes.