At present, traditional business models are gradually being replaced, and companies in different industries and fields are paying much attention to the innovation and reform of e-commerce business models. First of all, this article summarizes the e-commerce business model applicable to this article through the literature induction method, the detailed explanation of cross-border e-commerce business model, and the innovation of domestic and foreign business models. It categorizes business models and introduces cross-border import business. It also introduces the development process of cross-border e-commerce in China’s consumer market, business processes, and the success factors of cross-border e-commerce. And, it introduces an evaluation matrix to create a cross-valuation model. The final research results show that the cross-border e-commerce business model has been comprehensively evaluated based on the evaluation index system. From the evaluation indicators and weights, it can be concluded that the organizational value weight of the platform in the cross-border e-commerce business model accounts for the largest weight, occupying a weight of 0.6370. Among the secondary evaluation indicators, the weight of key business capabilities accounted for the largest proportion, reaching 0.8571.
With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.
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