## 4 Experiments and Analysis
### Experiments
#### 4.4.1 Experiments on the 13 Models  
The models tested included the basic Naive Bayes and \(k\)-nearest neighbors models, as well as more advanced models such as BERT, RoBERTa, and MacBERT. When combining CNN, RNN, and DPCNN with BERT, we found that BERT combined with DPCNN gave the best results. Additionally, to compensate for BERT's lack of masking capability when dealing with Chinese, ERNIE was also used for the classification of agricultural news, combined with DPCNN and BiGRU, respectively. In order to be more applicable to agricultural news and retain more text features, DPCNN was optimized by reducing two convolutional layers, in order to obtain DPCNN-upgrade. We extracted features using both BiGRU and DPCNN-upgrade, then fused the two extracted features, which were then classified to achieve better results. In addition, we attempted to combine Word2 Vec with TextCNN by first training Word2 Vec with word vectors, then inputting it into TextCNN, obtaining the final results through Softmax classification. The experimental results for the models are provided in Table 5.  
It can be concluded, from Table 5, that the best results were achieved with ERNIE + DPCNN for the fishery classification. The average length in the fisheries data set was the longest of the five data sets and, with ERNIE and DPCNN, many valid features were still preserved, such that good results were achieved. ERNIE achieved the best results for forestry classification, where the average length in the forestry data set was second only to that in the fisheries data set. Effective features could be extracted with good results when using only ERNIE. EGC achieved the best results in the planting and animal husbandry categories, where the average lengths in the plantation and livestock data sets were the shortest. In EGC, the data is encoded by ERNIE, followed by DPCNN-upgrade and BiGRU to extract different features. Then, the two parts are fused to obtain richer features for better results. The models based on BERT and ERNIE did not obtain very good results when classifying the side business category. Word2 Vec + TextCNN  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **Fisheries** & **Forestry** & **Planting** & \
\begin{tabular}{c} **Animal** \\ **Husbandry** \\ \end{tabular} & **Side Business** &
\begin{tabular}{c} **Average** \\ **Accuracy (ACC)** \\ \end{tabular} \\ \hline Native Bayes & 0.0700 & 0.0100 & 0.3700 & 0.5300 & 0.0600 & 40.00\% \\ KNeighbors & 0.0700 & 0.2600 & 0.2900 & 0.0100 & 0.0600 & 19.00\% \\ BERT & 0.6876 & 0.8067 & 0.7741 & 0.6599 & 0.3407 & 68.42\% \\ RoBERTa & 0.3260 & 0.4000 & 0.6469 & 0.6485 & 0.0088 & 55.03\% \\ MacBERT & 0.6114 & 0.7586 & 0.6262 & 0.8003 & 0.3597 & 66.51\% \\ BERT + CNN & 0.9503 & 0.8403 & 0.5976 & 0.7870 & 0.1616 & 72.06\% \\ BERT + BiGRU & 0.5738 & 0.8405 & 0.6496 & 0.8604 & 0.2727 & 69.41\% \\ BERT + DPCNN & 0.6918 & 0.8246 & 0.7469 & 0.6777 & 0.4250 & 69.18\% \\ ERNIE & 0.9735 & **0.9125** & 0.8564 & 0.9060 & 0.7087 & 88.00\% \\ ERNIE + DPCNN & **0.9777** & 0.9076 & 0.8469 & 0.9121 & 0.7047 & 88.00\% \\ ERNIE + BiGRU & 0.9737 & 0.9087 & 0.8473 & 0.9208 & 0.7010 & 88.18\% \\ Word2 Vec + TextCNN & 0.8000 & 0.8200 & 0.7900 & 08800 & **0.7700** & 82.41\% \\ EGC & 0.9701 & 0.8889 & **0.8959** & **0.9322** & 0.7130 & **89.29\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of experimental results for 13 models, with the best experimental results represented in bold.  
achieved the best results for side business, which contains many other categories that are not easy to distinguish. Word2 Vec first trains the text adequately and then combines it with TextCNN to extract features, in order to achieve the best results.