基于改进的BERT-BiGRU-Attention的农业科技政策分类模型

Classification Model of Agricultural Science and Technology Policies Based on Improved BERT-BiGRU-Attention

【目的】农业科技政策在农业科技活动中对科技创新、资源配置等起着重要的影响,体现了国家及地方对农业科技活动的方向性指导作用,对农业科技政策进行分类研究实现自动分类可以提升“产学研用”各利益相关者搜索与匹配农业政策的效率,快速找到与自身需求相关的科技政策信息。【方法】本文结合政策工具、政策对象及政策目标构建农业科技政策三维分类指标体系并建立山东省农业科技政策分类数据集,以及对Bert-BiGRU-Attention模型进行改进,在山东省农业科技政策分类数据集上对改进后的Bert-BiGRU-Attention模型进行性能评估及对比试验。【结果】改进后的Bert-BiGRU-Attention模型在山东省农业科技政策分类数据集上分类精确度更高,得到的F1值也优于其他对比模型。【结论】实验结果表明,本文所提出的模型在山东省农业科技政策数据集上的F1值为0.9650,在农业科技政策分类任务中表现良好。

[Objective]The agricultural science and technology policy plays an important role in scientific and technological innovation and resource allocation in agricultural science and technology activities,which presents the directional guidance to the national and local agricultural science and technology activities.The automatic classification of agricultural science and technology policies can improve the efficiency of searching and matching agricultural policies by the stakeholders in“production,education,research and application”,which enables quick finding of the science and technology policy information related to their needs.[Methods]Combining with policy tools,policy objects,and policy objectives,this paper constructs a three-dimensional classification index system of agricultural science and technology policies,establishes a classification data set of agricultural science and technology policies in Shandong Province,constructs an improved Bert-BiGRU-Attention model,and conducts model performance evaluation and comparative experiments with the improved model on the data set of agricultural science and technology policies in Shandong province.[Results]The improved Bert-BiGRU-Attention model achieves higher classification accuracy on the data set,and the obtained F1 value is also better than other models compared.[Conclusions]The experimental results show that the F1 value of the model proposed in this paper on the agricultural science and technology policy dataset of Shandong Province is 0.9650,which shows that the model performs well in the policy classification task.