E-commerce has grown considerably with increasing access to the Internet. As a means of e-commerce, the virtual brand community (VBC) has become one of the important channels for brand enterprises to promote products, enhance brand awareness and compete with enterprises in the digital economy. The most important task of any website is the provision of tailored information and services to satisfy client needs. This research aimed to evaluate the status of websites and services provided by Chinese mobile internet enterprises. Based on the Fogg behaviour model, initial criteria and sub-criteria were constructed, and a new hybrid multiple criteria decision-making (MCDM) model was used in this study that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and the analytic network process (ANP) method to determine the interaction between factors and to determine the weight of each factor. Finally, the PROMETHEE method was utilised to rank and develop the consumer interaction capability of the VBC of Chinese mobile internet enterprises. Once these perceptions were captured, among the three criteria determined for the evaluation of the VBC of different mobile internet enterprises, affective involvement was the most important criterion, followed by motivation and environment triggers. Moreover, the VBCs of three communication enterprises were evaluated and ranked to validate the proposed model.

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.