基于随机森林的葡萄酒品类智能分类及电商场景化落地研究

Research on Intelligent Classification of Wine Categories Based on Random Forest and Its Scenario-Based Implementation in E-Commerce

摘要: 随着葡萄酒线上消费市场扩张，传统人工品类鉴别已无法适配电商大批量品控需求，现有葡萄酒智能分类研究多聚焦实验室算法优化，与电商实际场景脱节，技术成果难以落地。本文以解决葡萄酒电商痛点为核心，以快速核验、低成本检测、轻量化部署为约束，开展智能分类模型构建与场景化落地研究。研究采用UCI葡萄酒标准数据集，通过双维度筛选确定类黄素、酒精含量、色调为核心分类指标，精简检测维度；基于随机森林与决策树构建轻量型融合模型，经网格搜索结合5折交叉验证优化超参数，平衡模型精度与效率。实验表明，该模型分类准确率达90.74%，可在普通设备部署，有效降低中小商家成本与门槛，易形成可复用的研究框架。本文研究成果为葡萄酒电商提供了高效智能品控方案，推动其向轻量化、智能化转型，也为食品电商算法场景化转化提供了参考，对食品行业智能化发展具有重要意义。

Abstract: With the expansion of the online wine consumption market, traditional manual category identification can no longer meet the large-scale quality control demands of e-commerce. Most existing studies on intelligent wine classification focus on the optimization of laboratory algorithms, which are disconnected from actual e-commerce scenarios, making it difficult to implement technical achievements. Centering on solving the pain points of wine e-commerce and taking rapid verification, low-cost detection and lightweight deployment as constraints, this paper conducts research on the construction of intelligent classification models and scenario-based application. Adopting the standard UCI wine dataset, this study determines flavonoids, alcohol content and hue as the core classification indicators through two-dimensional screening to simplify the detection dimensions. A lightweight hybrid model is constructed based on random forest and decision tree. Hyperparameters are optimized by combining grid search with 5-fold cross-validation to balance model accuracy and efficiency. The experimental results show that the classification accuracy of the model reaches 90.74%. It can be deployed on ordinary devices, effectively reducing the costs and technical thresholds for small and medium-sized merchants, and is conducive to forming a reusable research framework. The research findings provide an efficient and intelligent quality control scheme for wine e-commerce, promote its lightweight and intelligent transformation, and offer a reference for the scenario-based transformation of algorithms in food e-commerce. It is of great significance to the intelligent development of the food industry.