## 4 Experiment
### Experimental Results and Analysis  
#### 4.2.1 Experimental Results with Different Parameters  
Different datasets require different epochs for training, and a suitable epoch allows the loss to converge to a stable value. Choosing the best epoch allows the model to achieve the best results while reducing the training time. As shown in Figure 7, we explored the loss descent plots for different models with the same learning rate. The model loss converges faster until 10 epochs, and then the loss decreases smoothly with increasing epochs. At close to 20 epochs, all model losses have only a weak float. Therefore, we set the training epoch to 20, thus reducing meaningless training time. As shown in Figure 7, the experiment compares the loss reduction states of the three models. Trained with the same parameter settings, the lowest loss in the experiment is 0.2173 for the BiLSTM model, 0.1773 for the BiLSTM+GCN model, and 0.1430 for the BiLSTM+Attn+GCN model. compared with the BiLSTM model and the BiLSTM+GCN model, our proposed BiLSTM+Attn+GCN model converges faster and has the smallest loss value, indicating that the model predicts results more accurately and classifies better. Comparing (b) and (c) in Figure 7 shows the change in loss decline of the two models after incorporating the three pruning operations. The experimental results show that the loss of the model converges faster after adding the pruning operation. It shows that by adding pruning, the redundant weights in the model are removed, and computational efficiency is improved at the same time. The experiments compare the BiLSTM+GCN (add pruning) model and BiLSTM+Attn+GCN (add pruning) model. The latter has the fastest convergence speed and the lowest loss value, which indicates that the model has the best classification. The experiments also compare the effects of different learning rates on model performance. The learning rate comparison experiments are validated on a BAGCN model that includes a novel pruning operation. The learning rate directly affects the convergence state of the model, and choosing a suitable initial learning rate can lead to better model training. Figure 7: Comparison of training loss reduction for different models on the same dataset. (**a**) Loss reduction plots for different models with the same parameters. (**b**) Comparison chart of loss reduction for fusing three pruning operations on the BILSTM+GCN model. (**c**) Comparison chart of loss reduction for fusing three pruning operations on the BILSTM+GCN+Attn model. If the initial learning rate is too large, the model will not converge. If the initial learning rate is too small, the model will converge too slowly. As shown in Figure 8, the degree of convergence of model loss is different at different learning rates. When the learning rate is small, the model converges slowly. When the learning rate is larger, the loss of the model first decreases and then increases. Too large a learning rate leads to overfitting the data in the training set, which makes the model less generalizable. When the learning rate is \(8\times 10^{-5}\), the loss of the model is minimized, and the loss tends to smooth out as the number of training iterations increases, indicating that an appropriate learning rate can lead to better convergence of the model loss. As shown in Figure 9, this paper also verifies the comparison of model results at different learning rates. The experiments show that the F1 value of the model gradually increases to the maximum and then decreases as the learning rate increases. In the experimental results, Marco-P is 0.721, and Marco-R is 0.642 for a learning rate of 4 \(\times 10^{-5}\). Marco-P is 0.695, and Marco-R is 0.662 for a learning rate of 6 \(\times 10^{-5}\). The Marco-P decreased by 0.026 and the Marco-R increased by 0.020 at a learning rate of 6 \(\times 10^{-5}\) compared to the results for 4 \(\times 10^{-5}\).