# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 3 Methods
### Multilayer Integration  
The BGF model employs multilayer fusion in addition to using single-layer linear fusion. The process of multilayer fusion is shown in Figure 5.  
In the process of multilayer fusion, the first layer of fusion combines the output of BERT and the output of the GCN in the feature dimension to obtain the fused features. Specifically, the output vectors of BERT and the GCN are element-wise summed, and then the fused features are projected to the shared feature space using a linear layer. Finally, a nonlinear  
Figure 4: Linear fusion process. First, the output of BERT is spliced with traditional features introduced into a linear layer and then fused through a linear layer. Specifically, the output of BERT is dimensionalized through a linear layer, and then the traditional features are spliced with the dimensionalized BERT output to obtain the fused features. Finally, the fused features are mapped to the category space through a linear layer for subsequent work.  
Figure 5: Multilayer fusion process. First, the output of the BERT is fused with the output of the GCN by splicing the output of the BERT with the output of the GCN in the feature dimension, and then the first fusion is performed through a linear layer. Then, the first fused features are fused again to obtain the final fused features. Finally, the fused features are mapped to the category space for classification through a linear layer.  
activation function (ReLU) is applied to transform the mapped features nonlinearly. The formula for the first layer fusion is as follows:  
\[\text{fused\_features\_1}=\text{ReLU}(\text{W}_{1}\text{:}\text{fused\_features\_q}+ \text{b}_{1}) \tag{8}\]  
Among them, Linear_fusion_1 represents the first linear fusion layer, tasked with mapping the outputs of the BERT model and the GCN model into a shared feature space. Subsequently, the results undergo nonlinear transformation via the ReLU activation function, enhancing the model's nonlinear capability.  
In the second stage of multilayer fusion, the features from the first fusion undergo further refinement. Specifically, these features serve as inputs and are once again mapped into a shared feature space through a linear layer. A nonlinear transformation is then applied using the ReLU activation function. The formula for the second layer fusion is as follows:  
\[\text{fused\_features\_2}=\text{ReLU}(\text{Linear\_fusion\_2}(\text{fused\_features\_1})) \tag{9}\]  
where fused_features_2 denotes the second linear fusion layer, which maps the first fused features into a higher-dimensional feature space. Subsequently, the result undergoes another nonlinear transformation via the ReLU activation function, further enhancing the mode's nonlinear capability and feature representation complexity.  
The operation of multilayer fusion is used to transform and adjust the outputs of different levels appropriately to integrate information from different sources and generate the final representation. This multiple fusion approach helps the model maintain flexibility and expressiveness when dealing with multiple data types and complex relationships. It gradually enhances the representation of textual features and increases the nonlinear representation and fitting ability of the model, thereby improving the performance and robustness of the model in complex tasks.