## 5 Conclusions  
In this paper, we proposed a Chinese agricultural news classification model, E3W, based on a GreedySoup weighting strategy and a multi-model combination approach. E3W consists of a combination of four sub-models, where the outputs of the four sub-models are weighted to obtain the final classification results. The proposed model was tested on a self-built Chinese agricultural news data set. A total of 13 model comparison experiments indicated a significant improvement in the classification performance of our proposed model; in particular, E3W improved the average accuracy by 1.02%, the average precision by 1.62%, the average recall by 1.21%, and the average F1-score by 1.02%. Subsequent ablation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Avg. Accuracy** & **Avg. Precision** & **Avg. Recall Score** & **Avg. F1-Score** \\ \hline E2(EGC + Enire) & 0.9039 & 0.9080 & 0.9039 & 0.9042 \\ E2(EGC + Enire-Dpcnn) & 0.9046 & 0.9089 & 0.9046 & 0.9049 \\ E3(EGC + Enire + Enire-Dpcnn) & 0.9039 & 0.9080 & 0.9039 & 0.9042 \\ E1W(EGC + word2 vec-Textcnn) & 0.9039 & 0.9080 & 0.9039 & 0.9042 \\ E2W(EGC + Enire + word2 vec-Textcnn) & 0.9039 & 0.9080 & 0.9039 & 0.9042 \\ E2W(EGC + Enire-Dpcnn + word2 vec-Textcnn) & 0.9046 & 0.9089 & 0.9046 & 0.9049 \\  & **0.9061** & **0.9103** & **0.9061** & **0.9063** \\
**E3W(Ours)** & **(+0.15\%)** & **(+0.14\%)** & **(+0.15\%)** & **(+0.12\%)** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiments on E3W, with the best experimental results in bold and the number in brackets representing the comparison of the current model with the previous best model.  
experiments also validated that the E3W model was composed of an optimal combination of sub-models. The results presented above demonstrate the excellent classification capability of the E3W model, which further enhances the effectiveness of Chinese agricultural short text classification and provides a new strategy for subsequent text classification work, using a combination of models to improve the model performance in downstream tasks.  
The method proposed in this paper still has some limitations. For example, several more accurate models should be assessed before performing model combination. There are many model improvement experiments and comparison experiments, and more pre-preparation research needs to be developed, where the pre-preparation workload is expected to be high when the problem to be dealt with requires more models. In addition, when the combined model contains a large number of sub-models, more comparative experiments are required to obtain the most effective weights.  
Moreover, E3W still has some shortcomings, as it was less accurate in classifying side businesses than other categories. This is because the side business category comprises many elements, such as textiles, handicrafts, agro-processing, and other related industries. Further research is needed to more effectively classify side businesses, in order to accurately distinguish side businesses from other categories and improve the agricultural news classification accuracy.  
Conceptualization, Z.X. and S.Y.; methodology, Z.X. and S.Y.; software, S.Y.; validation, X.D., Z.X. and S.Y.; formal analysis, Z.X.; investigation, D.T. and Y.G.; resources, S.Y.; data curation, D.T.; writing--original draft preparation, Z.X. and S.Y.; writing--review and editing, X.D., Y.G. and Z.L.; visualization, Z.X.; supervision, X.D.; project administration, Z.X. and S.Y.; funding acquisition, Z.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Sichuan Province, China (Grant No. 2022 NSFSC0172).  
Not applicable.