## 4 Experiments and Analysis
### Data Set  
At present, a data set for Chinese agricultural news is lacking. The most famous data set for Chinese news classification is the THUCNews data set [43], which contains 740,000 news documents. This data set includes 14 categories of news, such as sports, education, and science and technology; however, agricultural news is absent. In the absence of a data set in a research direction, it is common to construct the required data set [44]; accordingly, we endeavored to construct the data set used in this paper. The data set constructed in this paper includes Chinese agricultural news headlines, collected using the Octopus software [45]. The data were obtained from agricultural news websites such as China Animal Husbandry Network, Ocean Information Network, Southwest Fishery Network, China Agriculture Network, and China Soybean Network. These websites are all very large agricultural websites in China, hosted by professional agricultural companies. The above websites provide professional and objective news and perfect information, and have great influence in the Chinese agricultural community. The data set collected in this paper includes up to the latest data in 2022, which ensures the data currency of this data set, which is crucial in ensuring data quality [46; 47; 48]. The E3W model takes text data as input, including exclamations, special characters, etc. Therefore, we pre-process the data set, including the deletion of stop words and word segmentation. The deletion of stop words involves removing meaningless symbols, such as '[' and '', as well as semantic words that have no real meaning, such as 'etc','so on', and 'the'. These stop words and symbols provide no information, and their removal can help us to reduce the size of the training data, in order to capture the meaning more appropriately. Furthermore, word segmentation is the process of dividing Chinese text into entities and phrases, such that more of the meaning of the text can be retained for classification. The data set included 15,548 news headline data, collected from websites in different domains. Some manual checks were performed after data collection so that the data could be more correctly classified into the appropriate categories. Chinese agricultural classifications are generally divided into five categories; hence, some studies on Chinese agricultural news have also treated it as a five-category NLP task [19]. The exact number of categories is shown in Table 1. The ratio of data in the training, test, and validation sets was 8:1:1, and the average Chinese word length of sentences in the data set was 18.98. The details of the data set are provided in Table 1.  
For a more visual representation of the data set constructed for this paper, we provide word clouds for the five categories of the data set, as well as for the total data, in Figure 12.  
Table 2 shows a selection of the self-built Chinese agricultural news data set, presented in English. As can be seen from Table 2, the average length in the data set was short, resulting in insufficient text features being extracted and making classification more difficult. It is difficult for traditional models to achieve the highest classification accuracy in every category, which was considered as an important factor in this paper, leading to the use of multiple models for processing, through which we improved the classification accuracy by combining multiple sub-models. Among them, the text length for the planting category was the shortest, while there was not much difference between the text lengths in other categories. It is worth noting that the side business category texts were the most extensive and partially similar to the texts in the other categories, which is an important reason why the accuracy rate for the side business category was lower than those for the other categories in the classification results.