# Research on Long Text Classification Model Based on Multi-Feature Weighted Fusion
## 4 Discussion  
### Discussion of Experimental Results on the THUCNews Dataset  
From Figure 4, it can be seen that when a single model is used for the classification task, the convolutional pooling of the conventional convolutional neural network and the bi-directional extraction of the BiLSTM perform similarly in the text processing, so it can be seen that the difference between the convolutional neural network and the recurrent neural network classification results is not large, and both local and global features perform well in the text classification task.  
Compared with the traditional convolutional neural network TextCNN, the deep pyramidal convolutional neural network DPCNN model using deep convolution has a more dominant performance on the classification task; the reason is that its deep convolutional network can better learn the long-range dependencies of text sequences so that the deep  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Macro-F (\%)** \\ \hline TextCNN & 92.36 & 92.32 \\ BiLSTM & 92.84 & 92.91 \\ DPCNN & 92.97 & 92.94 \\ BiLSTM+CNN & 94.29 & 94.33 \\ BERT & 94.41 & 94.42 \\ BTCDC & 96.08 & 96.09 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on Sohu News dataset.  
Figure 4: Accuracy of each model on both datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **THUCNews** & **SouHuNews** \\ \hline BTCDC & 95.27 & 96.58 \\ BTCDC-ATT & 94.12 & 93.14 \\ BTCDC-MC & 94.36 & 93.51 \\ BTCDC-B & 93.85 & 92.74 \\ BTCDC-EP & 94.68 & 93.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of each model on the ablation experiment.  
network will perform better in feature extraction. In the paper [42], the deep confidence network in the text classification task significantly outperforms the classical algorithm. And with the BERT training model in the process of powerful pre-training, it can obtain better text semantic information. After fine-tuning the classification task, the combination of a fully connected layer and Softmax classifier can also achieve better results. The reason is that the Transformer structure in the model, combined with the pre-training method and self-attention mechanism, achieves better results in text context semantic learning association. In one of the papers [43], it is again verified that the pre-training approach based on a large corpus can enhance the classification effectiveness by improving the word embedding representation.  
By fusing the global feature representation of the BiLSTM with the local features extracted by the convolutional network, the classification results are better compared to other single-feature models. The bidirectional long and short-term memory network can obtain global semantic features and location information of the text sequences. More information in the text can be obtained through the dual feature representation combined with the convolutional network. Research [44] verified the effectiveness of local features with global semantics for the classification task.  
The text classification task often has a range effect of both global and local features on the classification effect, and incorporating certain semantic information can improve the classification performance of the text. In this model, after splicing global features with multi-level weighted local features, the global features are obtained while also focusing on some important local features. In addition, the local features contain different multi-level features, which can obtain a richer semantic representation of text features, and combined with deep isometric convolution and pooling, can better capture the text's long-range dependencies. Compared with other comparison models, it achieves better classification results on the THUCNews data set.