## 2 Background
### Related Work
#### 2.2.3 EGC  
Based on a combination of the ENIRE, BiGRU, and DPCNN-upgrade models and proposed by [PERSON] et al. in 2022, EGC is a model for agricultural news classification that achieved the best classification accuracy on Chinese agricultural news.  
EGC is divided into four parts, consisting of an input coding layer, a feature extraction layer, a feature fusion layer, and Softmax activation. At the input encoding layer, the input text is masked by ERNIE, embedded, and finally fed into the transformer for encoding. The encoded text is then fed into the feature extraction layer, which feeds the data into the DPCNN-upgrade and BiGRU modules for feature extraction. The extracted features are then fed into the feature fusion layer, where they are stitched together and fused into new features. The stitched features are then classified using Softmax, in order to obtain the final result. The EGC structure is shown in Figure 4.  
The GRU model contains two gate structures: the update gate and the reset gate [32]. The reset gate determines how new input information is combined with previously stored data, while the update gate indicates the amount of previous memories saved at the current time step. Compared to the LSTM model [33], the GRU model is faster to train and better able to represent text features. Let the input be \(Xt\) and the output of the GRU hidden layer  
Figure 4: EGC model structure.  
Figure 3: DPCNN model structure.  
at moment \(t\) be \(Ht\). W is the weight matrix connecting the two layers, the subscripts \(r\) and \(Z\) denote the reset and update gates, respectively, and \(\sigma\) denotes the activation function. The calculation formula is as follows:  
\[Z_{t}=\sigma(W_{2}\times[H_{t-1},X_{t}]) \tag{1}\]  
\[R_{t}=\sigma(W_{r}\times[H_{t-1},X_{t}]) \tag{2}\]  
\[\widetilde{H}_{t}=\tanh(W\times[R_{t}H_{t-1},X_{t}]) \tag{3}\]  
\[H_{t}=(1-Z_{t})H_{t-1}+Z_{t}\widetilde{H}_{t} \tag{4}\]  
The model uses BiGRU for data extraction. As news texts are typically short, the relationships between contexts need to be extracted in order to fully capture the semantic information of the headlines. However, GRU only extracts the impact of the preceding text on the following text and does not reflect the impact of the following text on the preceding text. The output of each step of BiGRU includes a combination of the forward and backward states of the current state, which better considers the relationships between the contexts such that more complete and rich feature information can be extracted [34]. The BiGRU structure is shown in Figure 5.  
DPCNN-upgrade is an improvement to the deep pyramidal convolutional neural network DPCNN proposed by JR; in particular, it is improved with regard to the features of short agricultural news. According to our calculations, the average length of the agricultural news data set in this paper was about 18.98 words, indicating that agricultural news is generally shorter than other news. In terms of this feature, DPCNN-upgrade reduces the two convolutional layers of DPCNN to retain more text features, thus achieving better results[PERSON] The DPCNN-upgrade structure is shown in Figure 6.  
Figure 5: BiGRU structure.  
Figure 6: Comparison between DPCNN and DPCNN-upgrade structures.