## 3 Results
### Comparison with Baseline Models
#### 3.3.2 Comparison Analysis  
We compared the classification accuracies of the CAC model and several baseline models on each dataset. The results showed that our proposed model achieved higher accuracy than the other models on most datasets. Table 2 summarizes the accuracy results of different baseline models compared to our proposed model on each dataset. Based on these experimental results, we can conclude that the CAC model has a high performance in text classification tasks and can serve as a strong benchmark model for future research and applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Yelp Review Polarity** & **AG’s News** & **Yelp Review Full** & **DBPedia** \\ \hline Word-level CNN & 95.40 & 91.45 & 59.84 & 98.58 \\ Char-level CNN & 94.75 & 90.49 & 61.60 & 98.34 \\ VDCNN & 95.72 & 91.33 & 64.72 & 98.71 \\ \hline LSTM & 94.74 & 86.06 & 58.17 & 98.55 \\ D-LSTM & 92.60 & 92.10 & 59.60 & 98.70 \\ \hline Bi-BloSAN & 94.56 & 93.32 & 62.13 & 98.77 \\ LEAM & 95.31 & 92.45 & 64.09 & 99.02 \\ \hline Our Model & 96.62 & 93.64 & 65.38 & 98.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%) of different models on different datasets. To provide a more intuitive visualization of the performance differences between different models on the datasets, we plotted the comparison results in a bar chart, as shown in Figure 5. This visualization method allows for a clearer view of the classification accuracy performance of different models on different datasets. Based on the experimental results above, we can see that our model has a significant advantage in classification accuracy compared to CNN-based text classification models. Our model outperforms both shallow CNN models (word-level and char-level) and deep VDCNN models. This indicates that our model can better learn local features of the text through multi-scale CNN convolution, and better calculate global relationships through attention mechanisms. Additionally, our model is able to capture the local position order of words through CNN and enhance it by adding pre-embedded position vectors, thus better preserving the order information between words. Compared to RNN-based classification models, our model consistently outperforms standard LSTM and its improved variant (D-LSTM) on all tasks. This suggests that our model is more suitable for extracting sequential features compared to traditional RNN models, which is due to the incorporation of position vectors and CNN that provide advantages in extracting sequence features. In addition, our model introduces a multi-head attention mechanism that better avoids the problem of gradient vanishing during long sequence computation compared to RNN, enabling our model to extract features from long sequences more effectively. Compared to the attention-based classification models, our model also has a strong competitive advantage. Although LEAM is slightly ahead of our model on the DBpedia dataset, this may be due to the fact that LEAM uses joint training of word and label. However, our model performs stably on most datasets, because our model adopts a hierarchical structure to extract features from different dimensions and uses a local-to-global strategy to achieve better results in semantic feature extraction. In addition, our model can better utilize CNN convolution to learn local features of text and calculate global relationships through attention mechanism, thus achieving better performance. After conducting a series of comparative experiments, we can conclude that our proposed CAC model has a significant advantage in semantic feature extraction. This is because our model uses a parallel structure to extract features from different dimensions and adopts a strategy of first extracting local features and then computing global relationships, thus better capturing both local and global information of the text.