College of Economics and Management, Northeast Agricultural University, Modern Agricultural Development Research Center, Harbin 150030, China.
School of Management, Guangdong University of Education, Guangzhou 510303, China.
Comput Intell Neurosci. 2022 May 18;2022:6564014. doi: 10.1155/2022/6564014. eCollection 2022.
China Internet plus agriculture was first put forward in 2015 by the Chinese government's work report, laying the foundation for the development of Internet plus agriculture and promoting the rapid growth of e-commerce marketing of agricultural products. The combination of agricultural product marketing and e-commerce effectively reduces the intermediate links of agricultural product sales. Many e-commerce professional villages have sprung up in some rural areas across the country, and the number of rural e-commerce stores has continued to grow. At this stage, rural e-commerce has become a new way of agricultural trade, and rural e-commerce has formed a unique rural e-store. At present, the e-commerce market share of agricultural products in rural stores is very large, and its advantages are favored by the government, scientific research institutions, and agricultural products processing enterprises. However, with the gradual development of rural e-commerce, it has also encountered many difficulties. Based on this point, this study applies deep learning and data mining to optimize e-commerce marketing. First, with the growth of the online scale of agricultural product transaction data, the creation of traditional shallow model cannot meet the needs of online data processing. Therefore, this study decides to use the deep learning theory for optimization. It has excellent performance in the technical fields of big data processing and image and voice processing and has strong construction ability, which can effectively represent the characteristics of the model. Combined with the characteristics of e-commerce agricultural products processing and consumer practice, this study designs and develops a new customer value evaluation model based on data mining and e-commerce agricultural products value characteristics in the field of e-commerce. By combining deep learning and data mining technology, this study applies it to the field of e-commerce, so as to promote the transformation of marketing optimization.
中国“互联网+农业”于 2015 年首次由中国政府工作报告提出，为“互联网+农业”的发展奠定了基础，促进了农产品电子商务营销的快速增长。农产品营销与电子商务的结合有效减少了农产品销售的中间环节。全国各地农村涌现出许多农产品电子商务专业村，农村电子商务网店数量不断增长。现阶段，农村电子商务已成为一种新的农业贸易方式，农村电子商务形成了独特的农村网店。目前，农村网店农产品电子商务市场份额很大，其优势受到政府、科研机构和农产品加工企业的青睐。然而，随着农村电子商务的逐步发展，它也遇到了许多困难。基于这一点，本研究将深度学习和数据挖掘应用于电子商务营销优化。首先，随着农产品交易数据在线规模的增长，传统浅层模型的创建无法满足在线数据处理的需求。因此，本研究决定对其进行优化，采用深度学习理论。它在大数据处理、图像和语音处理等技术领域具有出色的性能，并且具有强大的构建能力，可以有效表示模型的特征。结合电子商务农产品加工和消费者实践的特点，本研究设计并开发了一种基于数据挖掘和电子商务农产品价值特征的新客户价值评估模型，应用于电子商务领域，从而促进营销优化的转变。
