## 4 Experiment
### Experimental Results and Analysis
#### 4.4.4 Result on the CCIPC Dataset  
Comparative experiments were conducted based on the CCIPC dataset proposed in this work. The results are presented in Table 6. The experimental results show that RQ-OSPTrans achieves an accuracy of 82.7% on the test set, significantly higher than those of other models. RQ-OSPTrans demonstrates better performance compared with the traditional HAN model, indicating the advantages of the pre-trained language models. BERT and ALBERT, as representative pre-trained language models, also achieved decent performance on this task, but 1.5% and 3.8% gaps remain compared with RQ-OSPTrans. Models, such as RoBERTa, RBT6, and ERNIE, exhibit relatively lower accuracy on the test set, which may be due to their insufficient capability to learn semantic information from text containing numerous colloquial expressions and noise. ELECTRA and MacBERT still \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **\(M_{padding}\)** & **Test** \\ \hline HAN [13] & & 91.1 \\ BERT [1] & & 92.3 \\ ALBERT [25] & & 90.3 \\ RoBERTa [24] & & 92.3 \\ ELECTRA [53] & 256 & 94.3 \\ GPT-2 [54] & & 93.5 \\ RBT6 [26] & & 93.2 \\ MacBERT [26] & & 93.6 \\ SHGCN [47] & & 88.3 \\ \hline
**RQ-OSPTrans (ours)** & & **94.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of RQ-OSPTrans and state-of-the-art pre-trained models on the AG’s News dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **\(M_{padding}\)** & **Test** \\ \hline [PERSON] [13] & & 74.6 \\ [PERSON] [1] & & 83.4 \\ [PERSON] [25] & & 80.5 \\ [PERSON] [24] & & 77.9 \\ ELECTRA [53] & 512 & 83.2 \\ GPT-2 [54] & & 82.9 \\ RBT6 [26] & & 79.5 \\ MacBERT [26] & & 81.7 \\ Protoformer [45] & & 79.4 \\ \hline
**RQ-OSPTrans (ours)** & & **84.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of RQ-OSPTrans and the baseline models on the arXiv-10 dataset. fall short of RQ-OSPTrans. By contrast, XLNet performs the worst on the test set. Our RQ-OSPTrans model is better able to capture semantic information from text with the introduction of a residual-connected Transformer structure and further enhances performance on various pre-trained language models. Overall, the experimental results further validate the effectiveness and superiority of our proposed RQ-OSPTrans model in text topic recognition tasks. RQ-OSPTrans not only significantly outperforms other pre-trained language models in terms of performance but also demonstrates strong generalization capabilities, making it suitable for various types of text data. Compared with other comparison experiments, the label distribution of the CCIPC dataset is uneven. To better illustrate the improvement of RQ-OSPTrans over the baseline model, we compare the classification performance of the top five performing models in Table 6 on each label in this dataset, and the results are shown in Table 7. Here, we populate the header with an abbreviated representation of the dataset label. Experimental results show that the accuracy of our proposed method in the four categories is higher than that of other baseline models. From the overall analysis of the experimental data, the classification performance on the \"SIL\" label is the best, while the classification performance on the \"VP\" label is the worst. The direct cause of this result is the uneven distribution of labels: the \"SIL\" label has more than 1700 pieces of data, while the \"VP\" label has only 277 samples, and this huge difference in data volume makes it easier for the model parameters to fit the classification cues and weights of the \"SIL\" label, and the classification cues of the \"VP\" label may be ignored as unimportant information in repeated questions or masked in the auxiliary masking mechanism.