## 4 Experiments and Analysis
### Experiments
#### 4.4.2 Experiments with E3W and Four Sub-Models  
Considering Table 5, we obtained four sub-models with excellent classification results: ERNIE, ERNIE + DPCNN, EGC, and Word2 Vec + TextCNN. We combined these four high-performing models to obtain the proposed E3W model. Then, we evaluated these five models, in terms of accuracy (ACC), precision (P), recall (R), and F1-score (F1), in order to evaluate their effectiveness and conducted relevant ablation experiments, the details of which are provided below. E3W is the model proposed in this paper. In Figure 13, we provide the confusion matrix for E3W; it can be seen, from the figure, that the number of misclassifications for side business was the highest among all of the classification areas. In particular, the distinction between side business and planting categories was the most difficult, as some side business-related industries, such as the textile industry, often use materials from the plantation industry, making them prone to misclassification. There are also difficulties in distinguishing between side business and animal husbandry for similar reasons, such as the leather sector, which obtains its raw materials from animal husbandry. These are the key reasons underlying the low classification accuracy of side business. Based on the presented confusion matrix, we also obtained the ACC, P, R, and F1 values for E3W, which will be shown subsequently for comparison. We next verified whether the overall performance of the model was improved by analyzing the average accuracy. Table 6 shows the average accuracies of different models. The E3W model proposed in this paper achieved the highest average accuracy, being 1.02% higher than that of the most advanced EGC. It can be concluded that the classification effect of the E3W model is excellent, and it is more effective than using any model alone. Figure 13: Confusion matrix for E3W. Figure 14 shows the precision of the different models. It can be seen that E3W achieved the highest precision values for fisheries and planting, with increases of 0.02% and 1.59%, respectively. E3W achieved an average precision of 93.09%, which was 1.62% better than the state-of-the-art (EGC). Figure 15 shows the recall of the different models. Again, E3W had the highest recall rates in fisheries, animal husbandry, and side business categories, improving by 0.78%, 0.81%, and 1.44%, respectively. E3W reached 90.61% in the average recall rate, ahead of the state-of-the-art by 1.21%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Avg. Accuracy** \\ \hline ERNIE + DPCNN & 0.8895 \\ ERNIE & 0.8728 \\ EGC & 0.8959 \\ Word2 vec + DPCNN & 0.8526 \\ E3W(ours) & **0.9061** \\ **(+1.02\%)** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of average accuracy for E3W and the four sub-models, with the best experimental results in bold and the number in brackets representing the comparison of the current model with the previous best model. Figure 14: Comparison of precision for E3W and the four sub-models, with the best experimental results in bold and the number in brackets representing the comparison of the current model with the previous best model. Figure 16 shows the F1-scores of the different models, which reflect the overall capability of a model. E3W achieved the highest F1-score values in the fisheries, planting and side business categories, improving by 0.47%, 0.08% and 2.62%, respectively, with the average F1-score of E3W improving upon the state-of-the-art by 1.02%. Figure 16: Comparison of F1-scores for E3W and the four sub-models, with the best experimental results in bold and the number in brackets representing the comparison of the current model with the previous best model. Figure 15: Comparison of recall for E3W and the four sub-models, with the best experimental results in bold and the number in brackets representing the comparison of the current model with the previous best model. To verify the effectiveness of the E3W model combination, we conducted ablation experiments.