# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Experimental Analysis
#### 4.5.3 Error Analysis  
We further analyzed the misclassified samples to identify the differences between the BGF model and other models in error classification. Error type: We analyzed the models' performance in handling different types of texts, particularly those containing complex semantic relations.  
On the R8 dataset, the BGF model demonstrates higher accuracy when dealing with texts that have complex semantic relations, whereas the TextGCN model often misclassifies such texts. This is because the BGF model effectively captures both local and global semantic information through multilayer fusion, leading to better performance in handling complex semantic relations.  
By combining the output features of the BERT model and the GCN model, the BGF model can capture both local semantic information (via BERT) and global semantic information (via GCN). This comprehensive understanding of textual content results in improved classification accuracy.  
Overall, the _t_-test results indicate that the performance improvement of the BGF model is statistically significant on the R8 and R52 datasets but not significant on the MR dataset. The confidence interval results demonstrate the stability of the model's performance. Through misclassification analysis, we found that the BGF model excels in processing texts with complex semantic relations, further validating its improvement.  
The experimental comparison and analysis show that the BGF model effectively combines the output features of the BERT and GCN models through multilayer fusion. This enables the model to capture both local and global semantic information, leading to a more comprehensive understanding of text content and improved classification accuracy. In this paper, the output features of the BERT model and the GCN model are fused to enhance the model's ability to express the semantic information of the text and to improve the performance of the text classification task. From the experimental results, it can be seen that, compared with the TextGCN and BertGCN models, the accuracy of the BGF model on the R8 dataset is improved by 1.38% and 0.28%, respectively; on the R52 dataset, the accuracy of the BGF model is improved by 0.21% and 0.9%, respectively; on the MR dataset, the BGF model is improved by 9.53% compared to the TextGCN, and, compared to the BertGCN, it is only improved by 0.03%. By comparing the performance of the BertGCN and BGF models on several datasets, it was found that, on the two datasets R8 and R52, the loss function of the BGF model is smaller and more stable, indicating that the BGF model learns more effective information. The increase in accuracy and the decrease in cross-entropy loss function are key metrics for evaluating the performance of the classification model. The increase in accuracy indicates that the BGF model's ability to correctly categorize samples in the text categorization task is enhanced, reflecting the model's improved decision-making accuracy when processing data. The reduction in the cross-entropy loss function, on the other hand, indicates that the model optimizes the difference between the output probability distribution and the real labels, which suggests that the BGF model adjusts the parameters more efficiently during the learning process, which makes the BGF model's abstraction and understanding of the data features more accurate. The comparison experiments show that the BGF model improves its accuracy and prediction precision in the classification task by learning a more optimized feature representation during the training process.