中国电力2025，Vol.58Issue(11)：156-163,8.DOI:10.11930/j.issn.1004-9649.202411021
基于双层注意力机制的电力文本分类模型
A Power Text Classification Model Based on Dual-layer Attention Mechanism
摘要
Abstract
There are a large amount of Chinese text data in the electric power field,traditional text mining methods are faced with problems such as difficulty in word segmentation,limitations in text feature representation,and poor performance in handling complex relationships in text,which limit the deep understanding and classification of power information.This paper proposes a power text classification model that combines text convolutional neural networks(TextCNN)and Attention mechanism.A hierarchical optimization design was carried out for the input layer,TextCNN layer,first attention layer,pooling layer,second attention layer,and output layer,with experimental validation conducted to verify the model's performance.The results show that the proposed TextCNN-Attention model achieved a text classification accuracy of 96.8%,with a precision of 86.3%,a recall of 90.3%,and a F1 score(comprehensive evaluation metric)of 88.2%on the power text dataset,demonstrating the superior performance of the TextCNN-Attention model in processing power texts.This study can provide valuable experiences for application of deep learning in power text classification.关键词
深度学习/电力领域/文本分类/卷积神经网络/注意力机制Key words
deep learning/electricity sector/text classification/convolutional neural networks/attention mechanism引用本文复制引用
武同心,纪鑫,杨成月,陈屹婷,杨智伟..基于双层注意力机制的电力文本分类模型[J].中国电力,2025,58(11):156-163,8.基金项目
国家电网有限公司科技项目(基于图神经网络和图深度学习的电力知识抽取技术研究,52999021N005). This work is supported by Science and Technology Project of SGCC(Research on Power Knowledge Extraction Technology Based on Graph Neural Network and Graph Deep Learning,No.52999021N005). （基于图神经网络和图深度学习的电力知识抽取技术研究,52999021N005）
