Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion
Under the rapid development of agricultural information technology, there has been an explosive growth in agricultural Q&A texts on the Internet, making the accurate classification of these texts a critical research topic in this field. However, the dispersion, complexity, and difficulty...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858722/ |
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| author | Xuliang Duan Yuhai Liu Zhenjia You Zhiyao Li |
| author_facet | Xuliang Duan Yuhai Liu Zhenjia You Zhiyao Li |
| author_sort | Xuliang Duan |
| collection | DOAJ |
| description | Under the rapid development of agricultural information technology, there has been an explosive growth in agricultural Q&A texts on the Internet, making the accurate classification of these texts a critical research topic in this field. However, the dispersion, complexity, and difficulty in collecting agricultural data pose significant challenges to achieving efficient and accurate text classification. To address this, this paper proposes a text classification model based on multi-feature dynamic fusion—ERNIE 2.0_BiGRU_CapsNet_Att. The model uses the ERNIE 2.0 pre-training framework to encode the dataset to learn the dynamic semantic representations of words; it combines Bidirectional Gated Recurrent Units (Bi-GRU) and Capsule Networks to capture high-dimensional sequential and local features of the text; meanwhile, it enriches feature inputs by constructing artificial numerical feature vectors. With the introduction of an attention mechanism, the model can dynamically adjust feature weights to achieve effective multi-feature fusion. We validated the effectiveness of this fusion model on a self-constructed agricultural Q&A dataset. Experimental results, averaged over five runs, show that the proposed model achieved 95.1% precision (±0.1), 94.8% recall (±0.2), and 95.2% F1-score (±0.1), representing a 1.1% improvement in precision, a 0.6% improvement in recall, and a 0.7% improvement in F1-score compared to the best-performing baseline model. These results demonstrate that the proposed model significantly outperforms traditional classification models and achieves robust performance across experiments. |
| format | Article |
| id | doaj-art-df297c8d4f1e49ca993c2fdf06dd59f5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-df297c8d4f1e49ca993c2fdf06dd59f52025-08-20T02:49:20ZengIEEEIEEE Access2169-35362025-01-0113529595297110.1109/ACCESS.2025.353727710858722Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic FusionXuliang Duan0https://orcid.org/0000-0001-7559-346XYuhai Liu1https://orcid.org/0009-0004-3663-3835Zhenjia You2https://orcid.org/0009-0007-4275-5240Zhiyao Li3https://orcid.org/0009-0001-6749-4960School of Information Engineering, Sichuan Agricultural University, Yaan, Sichuan, ChinaSchool of Information Engineering, Sichuan Agricultural University, Yaan, Sichuan, ChinaKey Laboratory of Agricultural Information Engineering in Sichuan Province, Yaan, Sichuan, ChinaKey Laboratory of Agricultural Information Engineering in Sichuan Province, Yaan, Sichuan, ChinaUnder the rapid development of agricultural information technology, there has been an explosive growth in agricultural Q&A texts on the Internet, making the accurate classification of these texts a critical research topic in this field. However, the dispersion, complexity, and difficulty in collecting agricultural data pose significant challenges to achieving efficient and accurate text classification. To address this, this paper proposes a text classification model based on multi-feature dynamic fusion—ERNIE 2.0_BiGRU_CapsNet_Att. The model uses the ERNIE 2.0 pre-training framework to encode the dataset to learn the dynamic semantic representations of words; it combines Bidirectional Gated Recurrent Units (Bi-GRU) and Capsule Networks to capture high-dimensional sequential and local features of the text; meanwhile, it enriches feature inputs by constructing artificial numerical feature vectors. With the introduction of an attention mechanism, the model can dynamically adjust feature weights to achieve effective multi-feature fusion. We validated the effectiveness of this fusion model on a self-constructed agricultural Q&A dataset. Experimental results, averaged over five runs, show that the proposed model achieved 95.1% precision (±0.1), 94.8% recall (±0.2), and 95.2% F1-score (±0.1), representing a 1.1% improvement in precision, a 0.6% improvement in recall, and a 0.7% improvement in F1-score compared to the best-performing baseline model. These results demonstrate that the proposed model significantly outperforms traditional classification models and achieves robust performance across experiments.https://ieeexplore.ieee.org/document/10858722/Agricultural information technologyfusion modelcontextual featurelocal feature |
| spellingShingle | Xuliang Duan Yuhai Liu Zhenjia You Zhiyao Li Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion IEEE Access Agricultural information technology fusion model contextual feature local feature |
| title | Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion |
| title_full | Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion |
| title_fullStr | Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion |
| title_full_unstemmed | Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion |
| title_short | Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion |
| title_sort | agricultural text classification method based on ernie 2 0 and multi feature dynamic fusion |
| topic | Agricultural information technology fusion model contextual feature local feature |
| url | https://ieeexplore.ieee.org/document/10858722/ |
| work_keys_str_mv | AT xuliangduan agriculturaltextclassificationmethodbasedonernie20andmultifeaturedynamicfusion AT yuhailiu agriculturaltextclassificationmethodbasedonernie20andmultifeaturedynamicfusion AT zhenjiayou agriculturaltextclassificationmethodbasedonernie20andmultifeaturedynamicfusion AT zhiyaoli agriculturaltextclassificationmethodbasedonernie20andmultifeaturedynamicfusion |