摘 要
随着互联网的迅猛发展，文本数据呈爆炸式增长，如何高效准确地对海量文本进行分类成为亟待解决的问题。基于深度学习的文本分类技术凭借其强大的特征提取和表达能力展现出独特优势。本研究旨在探索深度学习在文本分类中的应用，以提升分类精度和效率。通过对比分析多种经典深度学习模型如卷积神经网络（CNN）、循环神经网络（RNN）及其变体长短期记忆网络（LSTM），并引入注意力机制优化模型结构，构建了适用于不同类型文本分类任务的新模型架构。实验采用多个公开数据集进行验证，在新闻分类、情感分析等典型场景下取得了优于传统方法的结果，平均准确率提升了约5% - 10%，尤其在处理长文本时效果显著。创新性地提出了一种融合多尺度特征的文本表示方法，有效解决了单一模型难以兼顾局部与全局语义信息的问题；同时设计了自适应调整策略以应对不同领域文本特性的差异，增强了模型泛化能力。研究表明，基于深度学习的文本分类技术具有广阔的应用前景，为后续研究提供了新的思路和技术参考。
关键词：深度学习 文本分类 卷积神经网络
Abstract
With the rapid development of the Internet, textual data has experienced explosive growth, making efficient and accurate classification of massive text volumes an urgent challenge. Deep learning-based text classification techniques have demonstrated unique advantages due to their powerful feature extraction and representation capabilities. This study aims to explore the application of deep learning in text classification to enhance accuracy and efficiency. By comparing and analyzing various classic deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and its variant Long Short-Term Memory networks (LSTM), and by incorporating attention mechanisms to optimize model architecture, a new model fr amework suitable for different types of text classification tasks has been constructed. Experiments were conducted using multiple public datasets, achieving superior results compared to traditional methods in typical scenarios such as news classification and sentiment analysis, with average accuracy improvements ranging from approximately 5% to 10%, particularly significant in processing long texts. An innovative multi-scale feature fusion text representation method was proposed, effectively addressing the issue of single models struggling to balance local and global semantic information. Additionally, an adaptive adjustment strategy was designed to accommodate the differences in characteristics across various domain texts, thereby enhancing model generalization capability. The findings indicate that deep learning-based text classification technology holds broad application prospects and provides new insights and technical references for future research.
Keyword:Deep Learning Text Classification Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2深度学习基础理论 2
2.1深度学习基本概念 2
2.2常用深度学习模型 3
2.3模型训练与优化技术 3
3文本分类关键技术 4
3.1文本预处理方法 4
3.2特征提取与表示 5
3.3分类算法选择与评估 5
4实验设计与结果分析 6
4.1实验数据集构建 6
4.2实验方案设计 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
随着互联网的迅猛发展，文本数据呈爆炸式增长，如何高效准确地对海量文本进行分类成为亟待解决的问题。基于深度学习的文本分类技术凭借其强大的特征提取和表达能力展现出独特优势。本研究旨在探索深度学习在文本分类中的应用，以提升分类精度和效率。通过对比分析多种经典深度学习模型如卷积神经网络（CNN）、循环神经网络（RNN）及其变体长短期记忆网络（LSTM），并引入注意力机制优化模型结构，构建了适用于不同类型文本分类任务的新模型架构。实验采用多个公开数据集进行验证，在新闻分类、情感分析等典型场景下取得了优于传统方法的结果，平均准确率提升了约5% - 10%，尤其在处理长文本时效果显著。创新性地提出了一种融合多尺度特征的文本表示方法，有效解决了单一模型难以兼顾局部与全局语义信息的问题；同时设计了自适应调整策略以应对不同领域文本特性的差异，增强了模型泛化能力。研究表明，基于深度学习的文本分类技术具有广阔的应用前景，为后续研究提供了新的思路和技术参考。
关键词：深度学习 文本分类 卷积神经网络
Abstract
With the rapid development of the Internet, textual data has experienced explosive growth, making efficient and accurate classification of massive text volumes an urgent challenge. Deep learning-based text classification techniques have demonstrated unique advantages due to their powerful feature extraction and representation capabilities. This study aims to explore the application of deep learning in text classification to enhance accuracy and efficiency. By comparing and analyzing various classic deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and its variant Long Short-Term Memory networks (LSTM), and by incorporating attention mechanisms to optimize model architecture, a new model fr amework suitable for different types of text classification tasks has been constructed. Experiments were conducted using multiple public datasets, achieving superior results compared to traditional methods in typical scenarios such as news classification and sentiment analysis, with average accuracy improvements ranging from approximately 5% to 10%, particularly significant in processing long texts. An innovative multi-scale feature fusion text representation method was proposed, effectively addressing the issue of single models struggling to balance local and global semantic information. Additionally, an adaptive adjustment strategy was designed to accommodate the differences in characteristics across various domain texts, thereby enhancing model generalization capability. The findings indicate that deep learning-based text classification technology holds broad application prospects and provides new insights and technical references for future research.
Keyword:Deep Learning Text Classification Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2深度学习基础理论 2
2.1深度学习基本概念 2
2.2常用深度学习模型 3
2.3模型训练与优化技术 3
3文本分类关键技术 4
3.1文本预处理方法 4
3.2特征提取与表示 5
3.3分类算法选择与评估 5
4实验设计与结果分析 6
4.1实验数据集构建 6
4.2实验方案设计 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
