电商平台在新媒体环境下面临用户行为碎片化与营销资源配置低效等困境，构建涵盖交易系统与社交平台及广告后台的多源数据融合架构，采集某头部电商平台180天内23.7万用户的行为数据，运用K-Means算法识别出5类差异化用户群体，结合XGBoost模型预测转化概率达83.7%的准确率，实验结果显示精准推送策略使转化率从14.2%提升至19.5%，增幅37.3%，用户生命周期价值增幅28.3%，广告ROI从1:2.8 优化至1:4.5，获客成本降低32.5%。动态调整机制实现策略实时响应为电商企业从经验驱动向数据驱动转型提供量化依据与可落地方案。

E-commerce platforms face challenges like fragmented user behavior and inefficient marketing resource allocation in new media. A multi-source data fusion architecture covering transactions, social platforms, and ads was built. Data from 237,000 users over 180 days was analyzed, identifying 5 user groups via K-Means and predicting conversion with 83.7% accuracy using XGBoost. Precision targeting boosted conversion by 37.3%, user lifetime value by 28.3%, ad ROI from 1:2.8 to 1:4.5, and reduced customer acquisition costs by 32.5%.