E-Commerce Marketing Optimization of Agricultural Products Based on Deep Learning and Data Mining

Area of Science:

• Agricultural Economics
• Data Science
• E-commerce Marketing

Background:

• The Chinese government's "Internet plus agriculture" initiative (2015) spurred rural e-commerce growth.
• E-commerce integration in agriculture reduces sales intermediaries and fosters rural e-stores.
• Despite growth, rural e-commerce faces significant challenges impacting its development.

Purpose of the Study:

• To optimize e-commerce marketing strategies for agricultural products.
• To address limitations of traditional models in processing large-scale agricultural transaction data.
• To develop an innovative customer value evaluation model for agricultural e-commerce.

Main Methods:

• Application of deep learning for big data processing and model optimization.
• Utilization of data mining techniques to analyze agricultural product value characteristics.
• Development of a novel customer value evaluation model integrating deep learning and data mining.

Main Results:

• Deep learning effectively processes large online transaction datasets, surpassing traditional shallow models.
• The developed model accurately evaluates customer value based on e-commerce agricultural product characteristics.
• The combined approach enhances marketing optimization within the agricultural e-commerce sector.

Conclusions:

• Deep learning and data mining offer powerful solutions for agricultural e-commerce marketing challenges.
• The new customer value model provides a robust framework for understanding and improving online sales.
• This research promotes the digital transformation of agricultural trade and marketing practices.