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Top1. Introduction
Due to my country's increasingly limited resources and environmental constraints, the traditional methods of large-scale investment, large-scale consumption, and extensive development are unscientific and unsustainable (Tsai, 2016). Economic growth returned to normal. The emergence of e-commerce breaks the limitations of time and space, changes the traditional forms of transactions, reduces circulation costs for the entire society, and brings more choices to consumers (Wu & Tsai, 2018). With the integration of emerging e-commerce and traditional industries, it is the transformation of traditional industries. Major breakthroughs have been made in upgrading and innovative development. Based on this background, my country must increase economic support for the emerging e-commerce industry and strengthen the development of e-commerce industry projects. For this reason, this article discusses the agglomeration mechanism of the emerging e-commerce industry from the perspective of social science. Analyze and explore. I hope it can be helpful to the country's economic planning and construction.
The advent of e-commerce will further overcome the limitations of traditional e-commerce in terms of time and area, and will enable people to conduct various e-commerce activities easily, quickly and safely. The popularity of emerging Internet e-commerce will significantly change people's life and work methods. Emerging e-commerce can fully meet the individual needs and preferences of consumers, and users can also choose their own equipment, as well as the way to provide services and information. The sociality of Xinxing Electronics in business is mainly reflected in the sociality of network-based culture, the extensive participation of users, and the socialization of credit models. In the emerging e-commerce environment, people are in a more open network. People's experience in participating in e-commerce activities will lead to more valuable links. Therefore, the social nature of emerging e-commerce will create new value.
In the e-commerce industry, there is increasing emphasis on Big Data Analysis (BDA). However, as a concept, there is still a lack of exploration, which hinders the development of its theory and practice. Explore the BDA in e-commerce through a systematic review of the literature. Zhu H proposed an explanatory framework that explored the defining aspects, unique characteristics, types, business value and challenges of BDA in the e-commerce field. The paper also sparked a broader discussion about future research challenges and theoretical and practical opportunities. Overall, the research results integrate different BDA concepts (for example, the definition, type, nature, business value and related theories of big data), and provide deeper insights for cross-domain analysis applications in e-commerce (Zhu et al., 2016). There is increasing emphasis on big data analysis (BDA) in e-commerce. However, as a concept, it still lacks exploration, which hinders its theoretical and practical development. Explore the BDA in e-commerce through a systematic review of the literature. Devaraj S proposed an explanatory framework that explored the defining aspects, unique characteristics, types, business value and challenges of BDA in the e-commerce field. The paper also sparked a broader discussion about future research challenges and theoretical and practical opportunities. Overall, the research results integrate different BDA concepts (for example, the definition, type, nature, business value, and related theories of big data) to provide deeper insights for cross-domain analysis applications in e-commerce. (Devaraj et al., 2002). Bing L has developed an unsupervised learning framework for extracting popular product attributes from product description pages from different e-commerce websites. Different from the existing information extraction methods that do not consider the popularity of product attributes, the proposed framework can not only detect popular product features from customer reviews, but also map these popular features to related product attributes (Bing et al., 2016).