## 4 Experiment
### Comparative Experiment Results and Analysis  
Firstly, a comparative experiment was carried out on the dataset self-labeled in this study. It mainly compared the relationship classification models based on the CNN and the attention-based and two-way long-short-term memory networks. At the same time, two relationship classification networks based on the fine-tuning of the output features of the BERT pre-trained model were compared. These two models had the same feature extraction process as the relationship classification model proposed in this study, and both were fine-tuned based on the output features of the BERT model to perform the relationship classification task. On the SemEval-2010 Task 8 dataset, the compared models mainly included the relationship classification network based on the long short-term memory network, the relationship classification model based on the convolutional neural network, and the relationship classification network based on the pre-trained model. In the KBP37 dataset, the compared models were mainly based on traditional neural network models, including the CNN and RNN. Attention-based correlation models were also compared with relation classification models proposed by recent work. The comparative test results on the three datasets are shown in Table 3.  
It can be seen from Table 3 that the model in this study achieved good results on the self-labeled standard text. The F1-score reached 78.8%, and compared with the CNN model, the F1-value increased by 8.79%. Compared with the CNN-based relation classification model CR-CNN, the F1-value increased by 6.8%. Compared with the BiLSTM-attention and multi-attention CNN based on bidirectional long-short-term memory network and attention mechanism, it increased by 3.7% and 2.7%, respectively. Compared with CRSAtt [37] and FA-RCNet [38] based on the BERT pre-trained model, it increased by 0.8% and 0.9%, respectively. The experimental results on the emEval-2010 Task 8 dataset showed that the relationship classification model proposed in this paper also achieved good results on the SemEval-2010 Task 8 dataset. The F1-value on the SemEval-2010 Task 8 dataset \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**F1**} \\ \cline{2-4}  & **Standard Text Dataset** & **SemEval-2010 Task 8** & **KBP37** \\ \hline CR-CNN & 72.0\% & 84.1\% & - \\ BiLSTM-Attention & 75.1\% & 84.0\% & - \\ Multi-Attention CNN & 76.1\% & 88.0\% & - \\ TACNN & 77.2\% & 85.3\% & - \\ CRSAtt & 78.0\% & 90.55\% & 69.55\% \\ FA-RCNet & 77.9\% & 90.33\% & 69.95\% \\ Entity Attention BiLSTM & - & 85.2\% & - \\ BLSTM + BTLSTM + Att & - & 87.1\% & - \\ Bi-LSTM+LET & - & 85.2\% & - \\ MALNet & - & 86.3\% & - \\ DesRC-CNN & - & 86.6\% & - \\ TRE & - & 87.1\% & - \\ R-BERT & - & 89.25\% & - \\ BERTEM + MTB & - & 89.5\% & - \\ RNN & 70.01\% & - & 58.8\% \\ CNN & 68.88\% & - & 55.1\% \\ BiLSTM-CNN & 73.24\% & - & 60.1\% \\ Structural block-CNN & - & - & 60.9\% \\ Att-RCNN & - & - & 61.83\% \\ Bi-SDP-Att & - & - & 64.39\% \\ MALNet & - & - & 61.4\% \\ LGCNN & - & - & 63.2\% \\ D-BERT & - & - & 69.2\% \\ \hline
**Our** & **78.8\%** & **89.75\%** & **70.28\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative test results on three datasets. reached 89.75%. Compared with the existing model, it improved to a certain extent. Compared with the TRE, R-BERT, and BERTEM + MTB models, which were also based on the pre-trained model, the F1-value of the model in this paper increased by 2.56%, 0.5%, and 0.25%, respectively. The experimental results on the KBP37 dataset showed that the relationship classification model proposed in this paper achieved an F1-value of 70.28% on the KBP37 dataset, and compared with the recently proposed relationship classification models MALNet, LGCNN, and D-BERT, the F1-value increased by 8.88%, 7.08%, and 1.08%. Among them, we integrated the data on the three datasets into an experimental result table. Model performance can be evaluated by comparing longitudinal data.