基于K-means聚类算法的电商数据智能分析方法设计

Design of Intelligent Analysis Method of E-Commerce Data Based on K-means Clustering Algorithm

为提高电商产业的经济效益,设计了一种基于K-means聚类算法的电商数据智能分析方法。应用网络爬虫技术挖掘并提取电商数据,对数据集进行预处理,再基于K-means均值聚类划分算法构建电商数据智能分析模型,实现电商数据的智能化分析。实验结果显示,设计方法的F1值较高,F1值的方差为1.204,表明设计方法具有优良的准确性与稳定性,可为电商行业的数字化运营提供一种有效的分析技术。

In order to improve the economic benefits of e-commerce industry, this paper designs an intelligent analysis method of e-commerce data based on K-means clustering algorithm. Apply the web crawler technology to mine and extract e-commerce data,preprocess the data set, build an intelligent analysis model of e-commerce data based on K-means clustering algorithm, and realize the intelligent analysis method of e-commerce data. The experimental results show that the F1 value of the design method is high, and the variance of F1 value is 1.204, which shows that the design method in this paper has excellent accuracy and stability, and can provide an effective analysis technology for the digital operation of e-commerce industry.