# Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion
## 4 Experiments and Results
### Experimental Comparison and Result Analysis  
#### 4.3.1 Model Comparison  
To verify the robustness of the BBLC model, the accuracy, precision, recall, and F1 score on the test set were compared across various models using multiple sets of experiments. Table 4 compares the experimental results of the BBLC model with other models on the test set. The experiment uses the F1 score as the primary evaluation metric, which simultaneously considers the model's precision and recall. According to Table 4, it can be observed that, compared to other models, the BBC model has the highest F1 score. A high F1 score indicates that the model has excellent performance in classification tasks and can effectively distinguish and predict various categories. Second, the recall rate is also the highest among them, and a high recall rate usually indicates that the model can find more correct predictions in real positive samples. Thirdly, even when the recall is at its highest, the model also exhibits the highest precision. This indicates that the model, when predicting positive samples, can minimize the misclassification of negative samples as positive, demonstrating better precision. Fourth, the high accuracy of a model indicates that the model can correctly predict more results in all samples and has better overall forecasting ability. These four highest metrics \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BERT-CNN [24] & 0.9509 & 0.9354 & 0.9503 & 0.9395 \\ BERT-BiGRU & 0.9345 & 0.9405 & 0.9126 & 0.9213 \\ BERT-BiLSTM [23] & 0.9306 & 0.9080 & 0.9270 & 0.9129 \\ BERT-BiGRU-CNN [28] & 0.9420 & 0.9385 & 0.9344 & 0.9322 \\
**BERT-BiLSTM-CNN** & **0.9536** & **0.9541** & **0.9519** & **0.9509** \\ \hline \hline \end{tabular} The bold text is the model of the article.  
\end{table} 
Table 4: Comparison of experimental results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Value** & **Hyperparameter** & **Value** \\ \hline Epoch & 15 & Optimizer & Adam \\ Dropout & 0.5 & Hidden_size & 768 \\ Learning rate & \(10^{-3}\) & Max_length & 300 \\ Batch\_size & 128 & Kernel\_sizes & [7,8,9] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental parameters. imply that the BBLC model has the most optimal overall performance. Figure 4 shows the accuracy and loss curves of the BBLC model on the training and validation sets. The confusion matrix in Figure 5 illustrates the classification performance of different models on the test set. According to the confusion matrix, the BERT-CNN model correctly classified 10,466 data points, BERT-BiLSTM classified 10,242, BERT-BiGRU-CNN classified 10,368, while this article's BBLC model classified 10,497, which exhibits the best performance among these four main models. Although the BBLC model misclassifies most one-class labels into two-class labels, its overall classification ability is the best, achieving the highest accuracy in ticket text classification. Figure 4: Training set accuracy and loss curves. Figure 5: Confusion matrices for different models. The attention mechanism in a hybrid neural network model usually allows the model to make greater use of features that are retrieved from different components. However, due to public service request text characteristics such as varying lengths--some are excessively long and some are too short, as well as the irregular use of punctuation--some public service request text lacking punctuation or with punctuation errors, and syntactical issues like incoherent sentence structures, the classification performance using attention mechanisms shows variability. To evaluate the effectiveness of the Attention mechanism in the public service request text classification task, the Attention mechanism is integrated into the hybrid neural network for comparison.