基于相关性模型与大数据分析的关键词优化方法研究OACSTPCD

With the rapid development of computer networks and information technology,e-commerce has experienced explosive development.E-commerce online store operation has become a technology pursued by a large number of e-commerce practitioners.With the popularization and application of big data technology,e-commerce online store operation technology is also constantly evolving,and keyword selection and optimization based on big data is an important technology that people pay attention to.The correlation model is the basic model for sorting product searches on e-commerce platforms.Search Engine Optimization(SEO)based on the correlation model is currently the basic method for keyword optimization and screening in e-commerce data-driven operations.We combine the original method with big data analysis to optimize and screen keywords from multiple dimensions such as keyword col-lection,screening,combination,monitoring,and exchange,and propose and discuss three optimization models for keyword selection:com-petition coefficient,number of single grade competitions,and adaptability of keyword subject price range.Conduct practical monitoring on the effectiveness of the selected keywords after selection optimization.A large amount of monitoring data shows that using the keyword selection optimization method combining correlation model and big data analysis to obtain product title keywords can effectively improve the exposure of the product click through rate and conversion rate,achieving more efficient online store traffic and transaction volume.