摘 要
随着大数据时代的到来，文本数据的规模呈爆炸式增长，传统的文本分类方法在面对海量、高维和复杂的数据时逐渐显现出局限性。为此，本研究基于深度学习技术提出了一种高效的大数据文本分类方法，旨在解决传统方法在特征提取与模型泛化能力方面的不足。具体而言，该方法结合卷积神经网络（CNN）和长短时记忆网络（LSTM）的优势，构建了多层次的特征提取框架，能够自动捕捉文本中的局部语义信息和全局上下文依赖关系。同时，为应对大规模数据处理需求，引入分布式计算策略优化模型训练过程，并设计了一种自适应学习率调整机制以提升收敛速度和分类精度。
关键词：大数据文本分类 深度学习 卷积神经网络
Abstract
With the advent of the era of big data, the scale of text data has exploded, and the traditional text classification method gradually shows its limitations in the face of massive, high-dimensional and complex data. To this end, this study proposes an efficient big data text classification method based on deep learning technology, aiming to solve the shortcomings of traditional methods in feature extraction and model generalization ability. Specifically, this method combines the advantages of convolutional neural network (CNN) and long and short memory network (LSTM) to build a multi-level feature extraction fr amework, which can automatically capture the local semantic information and global context dependence in text. At the same time, in order to meet the needs of large-scale data processing, a distributed computing strategy is introduced to optimize the model training process, and an adaptive learning rate adjustment mechanism is designed to improve the convergence speed and classification accuracy.
Keyword:Big Data Text Classification Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2深度学习基础理论与技术 2
2.1深度学习基本概念 2
2.2常见深度学习模型架构 3
2.3文本分类中的深度学习应用 3
2.4深度学习算法优化策略 4
3大数据文本分类关键技术研究 4
3.1大数据文本特征提取方法 4
3.2高效文本预处理技术 5
3.3基于深度学习的分类算法设计 5
3.4分类模型性能评估指标 6
4实验设计与结果分析 6
4.1实验环境与数据集选择 6
4.2不同模型对比实验设计 7
4.3实验结果分析与讨论 7
4.4性能优化与改进方案 8
结论 8
参考文献 10
致谢 11
