Research on agricultural short text classification based on improved BERT model

To enhance the accuracy and efficiency of agricultural short text classification and address the shortcomings of traditional methods in terms of domain specificity, information sparsity, and semantic ambiguity, this study proposes an agricultural short text classification method based on an improved BERT model. By adding a Dropout layer and a Linear layer to the BERT model, this approach strengthens the model's understanding of agricultural domain-specific terms and concepts. Experimental results show that the improved BERT model achieves excellent performance in agricultural short text classification tasks, especially in the classification of categories with larger data volumes, providing new ideas for the research of agricultural short text classification.