## 1 Introduction  
As the internet and big data continue to evolve, accessing online information more effectively has become an increasingly important issue [1, 2, 3]. Agriculture is currently rushing towards modernization and informatization [4], and the volume of agricultural news is increasing daily. However, many industries that are difficult to distinguish are involved in agricultural news, resulting in people needing to spend more time screening the required agricultural news [5], dramatically hindering the dissemination of agricultural news [6, 7, 8]. In this line, the correct classification of agricultural news can lead to the more accurate dissemination of some advanced agricultural technologies [9, 10, 11], such as bioenergy robots for agriculture [12] and specialist drones [13], which can be used in farming to provide more solutions for the development of agriculture. Therefore, disseminating agricultural news can significantly contribute to the development of agriculture, and is of great importance to modern agriculture [14]. However, few studies have focused on the classification of agricultural news. Therefore, accurately classifying agricultural news has become an urgent problem. In recent years, with scientific and technological development, natural language processing (NLP) has been developing rapidly [15]. As an important branch of NLP, text classification has also developed rapidly, and there have been many studies on news text classification. However, there is a lack of relevant research on agricultural news classification. [PERSON] et al. [16] have focused on improving a patent keyword extraction algorithm by using the distributed Skip-gram model, and proposed a new text classification keyword extraction method to improve the effectiveness of the text classification algorithm. [PERSON] et al. [17] have proposed a two-channel news headline classification model based on the enhanced representation through knowledge integration (ERNIE) pre-training model, through the use of ERNIE and BiLSTM-AT to extract text information, and deep pyramid convolutional neural networks (DPCNNs) to overcome the long-distance text dependency problem; their approach performed well in news text multi-classification applications. [PERSON] et al. [18] have proposed a deep learning model for hierarchical text classification of Urdu news. Their model uses Word2 vec to convert words into vectors, followed by LSTM networks to learn text features and perform the final classification. There is still relatively little research on the classification of Chinese agricultural news. [PERSON] et al. [19] have proposed a model based on ENIRE, BiGRU, and DPCNN-upgrade (EGC), in which the text is first encoded by ERNIE, followed by feature extraction by the DPCNN and bidirectional gating recurrent unit (BiGRU). Next, the extracted features are fused, and the fused features are finally classified by Softmax. This model has achieved the best results in a Chinese agricultural news data set so far. Almost all of the research methods have tweaked individual models and only improved the average accuracy of the classification, while not achieving the highest accuracy in all categories; as such, text classification has not yet reached the desired level of effectiveness. As there are five general categories of agricultural news, even the most advanced models are unable to achieve the highest classification accuracy in all categories. The fine-tuning process typically consists of two steps: first, fine-tune the various parameters of the model; second, keep the model that achieves the highest accuracy on the validation set, while abandoning the other models. These discarded models can also provide great utility in the field of classification. Research from Google has shown that, while not performing optimally overall, some models perform well on certain data sets, such that the accuracy can be improved by combining such models [20].