Based on environmental needs and technical support, in recent years, B2C e-commerce platform has developed rapidly, and the competition among platforms is increasingly intensified. At the same time, due to the emergence and overlapping application of new information technology, China's consumer market has undergone new changes. Changes in the consumer market make B2C e-commerce platforms face new challenges. Therefore, this paper analyzes the challenges faced by B2C e-commerce platforms in the new consumption environment, and based on the triangle model of marketing strategy, puts forward suggestions on marketing strategies of B2C e-commerce platforms in the new consumption context, hoping to provide ideas for the sustainable development of B2C e-commerce platforms.

Given that the Internet has promoted the overall transformation of international trade, the global marketing strategy of cross-border retail platform enterprises must be adjusted in accordance with the development of emerging Internet technologies and the digital business environment. The article aims to construct a new digital marketing model for crossborder retail platform enterprises based on STV marketing theory, which emphasizes on the target marketing, crossover marketing and forming platform alliances to earn the global market. With a goal of building a digital marketing ecosystem, the model offers detailed suggestions for each marketing strategy and integrates various factors at different stages of international development.

The Healthy Cities and Sustainable Development Goals are interlinked, and the Healthy Cities movement has received widespread response and support worldwide over the past 30 years. The European Healthy Cities Networks covers about 1,400 cities. China started the Healthy Cities Movement in 1994 and put forward the Healthy China Strategy in the report of 19th national congress of CPC. Based on the impact of COVID-19 and Henan floods on urban health, it is necessary to incorporate some unconventional indicators into the Healthy Cities evaluation indicator framework. In addition, structured data cannot fully describe certain indicators, so the collection of unstructured data is equally important.

With the continuous improvement of economic globalization and the penetration rate of e-commerce, the overall cross-border e-commerce industry is developing more rapidly. Based on industrial convergence perspective, this paper studies the integration boundary of cross-border e-commerce and logistics industry, then analyzes the necessity, motivation and influence factors of the integration, and finally puts forward the integration strategy to promote the further development of the current cross-border e-commerce and logistics industry.

【Significance】With the rapid development of e-commerce in the era of big data, its credit evaluation becomes more and more important. This paper combs and studies the existing research literature in the field of e-commerce credit evaluation, so as to achieve a comprehensive understanding of the follow-up research hotspots and trends in this field and provide ideas for the development of follow-up research.【Process】(1) Using the Database of CNKI as the sample source, CiteSpace visual analysis software was used to draw the collaborative network map of high-yield authors, cooperative network map of important institutions, cooccurrence network map of high-frequency keywords and the timezone map of recent five years of research.(2) In order to better understand this field, literature analysis method was used to conduct deep learning research on the papers with high citation and high quality in the sample data. 【Conclusion】At the present stage, the research in this field is in the bottleneck period. The research hotspots are mainly divided into three aspects: research on C2C e-commerce platform, research on evaluation system, and research on model establishment. The frontier trends mainly include the research under the background of big data, the system improvement of e-commerce Law, and the application of machine learning algorithm.