摘 要
随着信息技术的迅猛发展，大数据分析已成为理解消费者行为和优化营销策略的重要工具。本研究旨在通过整合多源数据，运用机器学习算法与统计建模技术，对消费者行为进行精准预测，并探讨其在营销实践中的应用价值。研究基于大规模真实消费数据集，采用深度学习框架结合时间序列分析方法，构建了动态消费者行为预测模型。结果表明，该模型能够显著提升预测精度，特别是在复杂场景下的个性化需求预测方面表现优异。此外，研究还提出了一种基于预测结果的智能化营销决策支持系统，可有效提高营销活动的针对性与转化率。本研究的创新点在于将非结构化数据纳入分析框架，并引入注意力机制以捕捉关键行为特征，从而为营销策略制定提供了更为全面的数据支撑。研究结论不仅验证了大数据分析在消费者行为预测领域的可行性，还为企业实现精准营销和资源优化配置提供了理论依据与实践指导。
关键词：消费者行为预测；大数据分析；机器学习
Abstract
With the rapid development of information technology, big data analytics has become a crucial tool for understanding consumer behavior and optimizing marketing strategies. This study aims to integrate multi-source data and employ machine learning algorithms along with statistical modeling techniques to achieve precise predictions of consumer behavior, while exploring its application value in marketing practice. Based on a large-scale real-world consumption dataset, a dynamic consumer behavior prediction model was constructed by combining deep learning fr ameworks with time-series analysis methods. The results demonstrate that this model significantly enhances prediction accuracy, particularly excelling in personalized demand forecasting under complex scenarios. Furthermore, the study proposes an intelligent marketing decision support system grounded in the predictive outcomes, which effectively improves the targeting and conversion rates of marketing campaigns. The innovation of this research lies in incorporating unstructured data into the analytical fr amework and introducing attention mechanisms to capture key behavioral features, thereby providing more comprehensive data support for the formulation of marketing strategies. The conclusions not only validate the feasibility of big data analytics in the domain of consumer behavior prediction but also offer theoretical foundations and practical guidance for businesses to achieve precise marketing and optimize resource allocation.
Keywords: Consumer Behavior Prediction；Big Data Analysis；Machine Learning
目 录
一、绪论 1
（一）研究背景与意义 1
（二）国内外研究现状分析 1
二、大数据分析技术在消费者行为预测中的应用 1
（一）数据采集与预处理方法研究 1
（二）机器学习算法在行为预测中的作用 2
（三）实时数据处理对预测精度的影响 3
三、消费者行为特征的多维度分析 3
（一）行为模式的时间序列分析 3
（二）社交媒体数据的情感分析应用 4
（三）跨平台数据整合与行为画像构建 4
四、基于大数据预测的营销策略优化研究 5
（一）精准营销模型的设计与实现 5
（二）动态定价策略的数据驱动方法 5
（三）用户留存与转化率提升的实践探索 6
结 论 6
致 谢 8
参考文献 9
