## 4 Experiment
### Experimental Results and Analysis
#### 4.2.2 Comparison Experiment  
To validate the performance of the BAGCN model, we checked the BiLSTM model and BiLSTM+GCN model against each other on a unified dataset. The experimental results are shown in Table 2. The F1 value of the BiLSTM model is 55.23%, and the F1 value of the BiLSTM+GCN model reaches 61.11%. This shows that the graph convolutional neural network can learn the dependencies in the sentences and the node information in the graph, thus improving the effectiveness of the model. The BiLSTM+Attn+GCN model has a higher \(p\)-value, R-value, and F1-value than the other models. Compared with the BiLSTM+GCN model, introducing a multi-head attention mechanism to learn sentence information from different semantic spaces and giving high attention scores to related entities can significantly improve the classification results. According to the experimental results, adding GCN and multi-head attention mechanism to BiLSTM can effectively improve the performance of relation classification model.  
Information about the different dependencies of the sentences can impact model training. Different pruning operations cause the model to learn different information about the sentences as well, which affects the results. Table 3 compares the results of different pruning operations of the two models, where B+G represents the BiLSTM+GCN model, and B+A+G represents the BiLSTM+Attn+GCN model proposed in this paper. Local denotes local pruning operation, SPT denotes shortest-path tree pruning operation, and Local+SPT denotes the fusion of two pruning operations. The F1 value for the B+G(Local) model is 60.44%, and the F1 value for the B+G(SPT) model is 61.18%. However, in Table 2, the F value of the B+G model is 61.11%. The F1 value of the B+G(Local) model is lower than that of the B+G model, which indicates that there may be information loss after adding local pruning to the B+G model. Because local pruning retains only the entities and their related nodes in the sentence, this leads to the neglect of important information in the sentence during pruning and reduces the relation classification results. The F1 of the B+G (Local+SPT) model was 62.17%, indicating that the B+G model incorporating the two pruning operations had the best performance. Fusion of the two pruning operations can retain the important information in the sentences and remove the useless information from the sentences. In addition, compared with the B+A+G model without pruning in Table 2, the F1 value of the B+A+G (Local+SPT) model increased by 1.14% When the B+A+G model incorporates only one pruning operation, the model does not work well. When the B+A+G model incorporates both pruning operations, the model achieves the highest  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{p}\) & **R** & **F1** \\ \hline BiLSTM & 61.05 & 54.30 & 55.23 \\ BiLSTM+GCN & 64.00 & 61.19 & 61.11 \\ BiLSTM+Attn+GCN & 70.98 & 64.52 & 66.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments with different models (unit: %).  
Figure 9: Comparison of experimental results of the model under different learning rates.  
\(p\)-value, R-value, and F1-value. This shows that the pruning strategy proposed in this paper can improve the performance of the model's relation classification.