基于深度学习的大数据分类算法研究
摘 要
随着信息技术的迅猛发展，大数据分析成为当今研究热点，传统分类算法在处理海量、高维数据时面临诸多挑战。为此，本研究聚焦基于深度学习的大数据分类算法，旨在探索更高效精准的数据分类方法。通过引入卷积神经网络与循环神经网络架构，结合自适应优化算法，提出一种新型混合深度学习框架。该框架能够自动提取复杂特征并实现多维度数据的有效融合。实验采用公开大规模数据集进行验证，结果表明所提算法在准确率、召回率及F1值等关键指标上均优于现有主流方法，特别是在处理非结构化数据方面展现出显著优势。创新性地引入注意力机制，使模型具备更强的解释性与鲁棒性，有效解决了传统深度学习模型“黑箱”问题。此外，针对实际应用场景需求，设计了轻量化版本算法，降低了计算资源消耗，在保证性能的同时提升了实用性。
关键词：大数据分类 深度学习 卷积神经网络
Abstract
With the rapid development of information technology, big data analysis has become a research hotspot, and traditional classification algorithms face many challenges when dealing with massive and high-dimensional data. To this end, this study focuses on big data classification algorithms based on deep learning, aiming to explore more efficient and accurate data classification methods. By introducing a convolutional neural network and recurrent neural network architecture and an adaptive optimization algorithm, we propose a novel hybrid deep learning fr amework. The proposed fr amework can automatically extract complex features and achieve an efficient fusion of multi-dimensional data. The experiment was verified using public large-scale datasets, and the results show that the proposed algorithm is better than the existing mainstream methods in key indicators such as accuracy, recall rate and F1 value, especially shows significant advantages in processing unstructured data. The attention mechanism is innovatively introduced to make the model more interpretive and robust, and effectively solve the "black box" problem of the traditional deep learning model. In addition, according to the requirements of practical application scenarios, the lightweight version of the algorithm is designed, which reduces the consumption of computing resources and improves the practicability while ensuring the performance.
Keyword:Big Data 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 1
1.3研究方法概述 1
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深度学习优化算法 3
3大数据分类算法分析 4
3.1传统分类算法局限性 4
3.2深度学习分类优势 5
3.3分类算法性能评估 5
4基于深度学习的改进算法 6
4.1改进算法设计思路 6
4.2关键技术实现方法 6
4.3实验结果与分析 7
结论 8
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，大数据分析成为当今研究热点，传统分类算法在处理海量、高维数据时面临诸多挑战。为此，本研究聚焦基于深度学习的大数据分类算法，旨在探索更高效精准的数据分类方法。通过引入卷积神经网络与循环神经网络架构，结合自适应优化算法，提出一种新型混合深度学习框架。该框架能够自动提取复杂特征并实现多维度数据的有效融合。实验采用公开大规模数据集进行验证，结果表明所提算法在准确率、召回率及F1值等关键指标上均优于现有主流方法，特别是在处理非结构化数据方面展现出显著优势。创新性地引入注意力机制，使模型具备更强的解释性与鲁棒性，有效解决了传统深度学习模型“黑箱”问题。此外，针对实际应用场景需求，设计了轻量化版本算法，降低了计算资源消耗，在保证性能的同时提升了实用性。
关键词：大数据分类 深度学习 卷积神经网络
Abstract
With the rapid development of information technology, big data analysis has become a research hotspot, and traditional classification algorithms face many challenges when dealing with massive and high-dimensional data. To this end, this study focuses on big data classification algorithms based on deep learning, aiming to explore more efficient and accurate data classification methods. By introducing a convolutional neural network and recurrent neural network architecture and an adaptive optimization algorithm, we propose a novel hybrid deep learning fr amework. The proposed fr amework can automatically extract complex features and achieve an efficient fusion of multi-dimensional data. The experiment was verified using public large-scale datasets, and the results show that the proposed algorithm is better than the existing mainstream methods in key indicators such as accuracy, recall rate and F1 value, especially shows significant advantages in processing unstructured data. The attention mechanism is innovatively introduced to make the model more interpretive and robust, and effectively solve the "black box" problem of the traditional deep learning model. In addition, according to the requirements of practical application scenarios, the lightweight version of the algorithm is designed, which reduces the consumption of computing resources and improves the practicability while ensuring the performance.
Keyword:Big Data 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 1
1.3研究方法概述 1
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深度学习优化算法 3
3大数据分类算法分析 4
3.1传统分类算法局限性 4
3.2深度学习分类优势 5
3.3分类算法性能评估 5
4基于深度学习的改进算法 6
4.1改进算法设计思路 6
4.2关键技术实现方法 6
4.3实验结果与分析 7
结论 8
参考文献 9
致谢 10
