摘 要
随着信息技术的迅猛发展和数字化转型的深入推进，大数据已成为推动社会进步和经济发展的关键资源，用户行为分析与预测作为大数据应用的重要领域，对于提升商业决策效率、优化用户体验以及实现个性化服务具有重要意义本研究以大数据环境下的用户行为分析与预测为核心，旨在通过整合多源数据和引入先进算法，构建一套高效、精准的行为分析框架研究基于大规模真实数据集展开，采用机器学习与深度学习相结合的方法，对用户行为模式进行建模，并结合时间序列分析和关联规则挖掘技术，深入揭示用户行为的动态特征和潜在规律研究结果表明，所提出的框架能够显著提高用户行为预测的准确性和时效性，尤其在复杂场景下表现出更强的适应能力此外，本研究创新性地引入了跨平台行为融合机制，有效解决了传统方法中数据孤岛问题，为更全面地理解用户行为提供了新思路主要贡献在于提出了一种综合考虑多维度特征的预测模型，该模型不仅提升了预测性能，还为实际应用场景提供了可操作性强的技术支持最终，研究结论强调了大数据技术在用户行为分析领域的巨大潜力，并为未来相关研究指明了方向
关键词：用户行为分析；大数据；机器学习；行为预测；跨平台融合
ABSTRACT
With the rapid development of information technology and the deepening of digital transformation, big data has become a critical resource for driving social progress and economic development. User behavior analysis and prediction, as an important domain of big data application, play a significant role in enhancing the efficiency of business decision-making, optimizing user experience, and achieving personalized services. This study focuses on user behavior analysis and prediction in the context of big data, aiming to construct an efficient and accurate behavioral analysis fr amework by integrating multi-source data and introducing advanced algorithms. Based on large-scale real-world datasets, the research employs a combination of machine learning and deep learning methods to model user behavior patterns, while incorporating time-series analysis and association rule mining techniques to reveal the dynamic characteristics and potential regularities of user behaviors. The results demonstrate that the proposed fr amework significantly improves the accuracy and timeliness of user behavior prediction, particularly exhibiting stronger adaptability in complex scenarios. Additionally, this study innovatively introduces a cross-platform behavior fusion mechanism, effectively addressing the data silo problem inherent in traditional methods and providing new insights for a more comprehensive understanding of user behaviors. The primary contribution lies in the development of a predictive model that comprehensively considers multi-dimensional features, which not only enhances prediction performance but also offers strong technical support for practical application scenarios. Ultimately, the study highlights the immense potential of big data technology in the field of user behavior analysis and provides directions for future related research.
Keywords: User Behavior Analysis; Big Data; Machine Learning; Behavior Prediction; Cross-Platform Integration
目 录
摘 要 I
ABSTRACT II
引言 1
第1章 大数据与用户行为分析基础 2
1.1 大数据技术概述 2
1.2 用户行为数据特征 2
1.3 数据采集与预处理方法 3
1.4 行为分析的核心挑战 3
第2章 用户行为模式识别方法 5
2.1 行为模式的定义与分类 5
2.2 基于机器学习的行为建模 5
2.3 时间序列分析的应用 6
2.4 聚类算法在行为识别中的作用 6
2.5 模式识别的评估指标 7
第3章 用户行为预测模型构建 8
3.1 预测模型的基本框架 8
3.2 数据驱动的预测方法 8
3.3 深度学习在预测中的应用 9
3.4 动态预测模型的设计与优化 9
3.5 预测结果的验证与调整 9
第4章 用户行为分析的实际应用 11
4.1 电子商务中的行为分析 11
4.2 社交媒体用户行为研究 11
4.3 在线教育中的个性化推荐 12
4.4 智能交通中的行为预测 12
4.5 行为分析对商业决策的支持 12
结论 14
参考文献 15
致 谢 16
