摘 要
随着信息技术的迅猛发展，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。基于机器学习的文本分类算法为这一问题提供了有效解决方案。本研究旨在探索不同类型机器学习算法在文本分类任务中的表现，以期提高分类精度和效率。通过对比分析传统机器学习算法（如朴素贝叶斯、支持向量机）与深度学习模型（如卷积神经网络、循环神经网络），并引入预训练语言模型优化特征表示，构建了融合多源信息的混合模型。实验结果表明，在多个公开数据集上，所提出的混合模型相较于单一模型具有更高的分类准确率，特别是在处理长文本和复杂语义场景时优势明显。此外，针对文本数据不平衡问题，提出了一种基于过采样的改进策略，有效缓解了少数类样本被忽略的现象。本研究不仅验证了预训练语言模型在文本分类任务中的重要性，还为后续研究提供了新的思路和技术参考，推动了文本分类领域的发展。
关键词：文本分类 机器学习算法 预训练语言模型
Abstract
With the rapid development of information technology, textual data has experienced explosive growth, making efficient and accurate classification of massive text volumes an urgent issue to address. Machine learning-based text classification algorithms offer effective solutions to this challenge. This study aims to explore the performance of different types of machine learning algorithms in text classification tasks in order to enhance classification accuracy and efficiency. By comparing traditional machine learning algorithms such as Naive Bayes and Support Vector Machines with deep learning models like Convolutional Neural Networks and Recurrent Neural Networks, and by incorporating pre-trained language models to optimize feature representation, a hybrid model integrating multi-source information was constructed. Experimental results demonstrate that, on multiple public datasets, the proposed hybrid model exhibits higher classification accuracy compared to single models, particularly when dealing with long texts and complex semantic scenarios. Furthermore, an improved strategy based on oversampling was proposed to address the problem of imbalanced text data, effectively alleviating the neglect of minority class samples. This research not only verifies the significance of pre-trained language models in text classification tasks but also provides new ideas and technical references for future studies, promoting the development of the text classification field.
Keyword:Text Classification Machine Learning Algorithm Pretrained Language Model
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文本分类基础理论 2
2.1文本分类基本概念 2
2.2机器学习算法原理 3
2.3特征选择与表示方法 3
3常用文本分类算法分析 4
3.1支持向量机算法应用 4
3.2决策树算法特点 5
3.3深度学习算法进展 5
4文本分类性能优化策略 6
4.1数据预处理技术 6
4.2模型调优方法 6
4.3性能评估指标 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，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。基于机器学习的文本分类算法为这一问题提供了有效解决方案。本研究旨在探索不同类型机器学习算法在文本分类任务中的表现，以期提高分类精度和效率。通过对比分析传统机器学习算法（如朴素贝叶斯、支持向量机）与深度学习模型（如卷积神经网络、循环神经网络），并引入预训练语言模型优化特征表示，构建了融合多源信息的混合模型。实验结果表明，在多个公开数据集上，所提出的混合模型相较于单一模型具有更高的分类准确率，特别是在处理长文本和复杂语义场景时优势明显。此外，针对文本数据不平衡问题，提出了一种基于过采样的改进策略，有效缓解了少数类样本被忽略的现象。本研究不仅验证了预训练语言模型在文本分类任务中的重要性，还为后续研究提供了新的思路和技术参考，推动了文本分类领域的发展。
关键词：文本分类 机器学习算法 预训练语言模型
Abstract
With the rapid development of information technology, textual data has experienced explosive growth, making efficient and accurate classification of massive text volumes an urgent issue to address. Machine learning-based text classification algorithms offer effective solutions to this challenge. This study aims to explore the performance of different types of machine learning algorithms in text classification tasks in order to enhance classification accuracy and efficiency. By comparing traditional machine learning algorithms such as Naive Bayes and Support Vector Machines with deep learning models like Convolutional Neural Networks and Recurrent Neural Networks, and by incorporating pre-trained language models to optimize feature representation, a hybrid model integrating multi-source information was constructed. Experimental results demonstrate that, on multiple public datasets, the proposed hybrid model exhibits higher classification accuracy compared to single models, particularly when dealing with long texts and complex semantic scenarios. Furthermore, an improved strategy based on oversampling was proposed to address the problem of imbalanced text data, effectively alleviating the neglect of minority class samples. This research not only verifies the significance of pre-trained language models in text classification tasks but also provides new ideas and technical references for future studies, promoting the development of the text classification field.
Keyword:Text Classification Machine Learning Algorithm Pretrained Language Model
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文本分类基础理论 2
2.1文本分类基本概念 2
2.2机器学习算法原理 3
2.3特征选择与表示方法 3
3常用文本分类算法分析 4
3.1支持向量机算法应用 4
3.2决策树算法特点 5
3.3深度学习算法进展 5
4文本分类性能优化策略 6
4.1数据预处理技术 6
4.2模型调优方法 6
4.3性能评估指标 7
结论 8
参考文献 9
致谢 10
