随着电子商务和实体经济的深度融合，物流行业迎来了前所未有的发展机遇，同时也面临着运营效率、服务质量等多方面的挑战。本文以物流运营数据为研究对象，通过数据分析与机器学习相结合的方法，对物流配送服务状况、销售区域潜力以及商品质量问题进行深入研究，旨在为物流运营优化提供数据支持和决策依据。研究过程中，首先对物流数据进行预处理，包括数据清洗、特征提取等操作；然后利用描述性统计分析方法探究不同维度下的物流运营特征；最后构建随机森林、K - means 聚类、支持向量机等机器学习模型，对配送服务问题、潜在销售区域和商品质量问题进行分析与预测。研究结果表明，所采用的方法能够有效识别物流运营中的关键问题和潜在机遇，为物流企业优化运营策略、提升服务质量提供了有力的参考。

With the in-depth integration of e-commerce and the real economy, the logistics industry has ushered in unprecedented development opportunities, while also facing challenges in various aspects such as operational efficiency and service quality. This paper takes logistics operation data as the research object, and conducts in-depth research on the status of logistics distribution services, the potential of sales regions, and commodity quality issues by combining data analysis and machine learning methods, aiming to provide data support and decision-making basis for logistics operation optimization. During the research process, first, the logistics data is preprocessed, including operations such as data cleaning and feature extraction; then, descriptive statistical analysis methods are used to explore the logistics operation characteristics from different dimensions; finally, machine learning models such as random forest, K-means clustering, and support vector machine are constructed to analyze and predict distribution service problems, potential sales regions, and commodity quality issues. The research results show that the adopted methods can effectively identify key problems and potential opportunities in logistics operations, providing strong references for logistics enterprises to optimize operational strategies and improve service quality.