At present, China’s foreign trade volume of cross-border e-commerce is still relatively small, the targeted legal system has not been established, and the industry access threshold is relatively low. The development of cross-border e-commerce faces many risks. Relying on data mining technology, data docking and risk control. At the data level, the risk of cross-border e-commerce transaction is processed and analyzed, and the potential risk is predicted, and more targeted risk control methods are proposed to reduce the risk of cross-border e-commerce transaction business. Aiming at this problem, this paper establishes the risk data analysis of cross-border e-commerce transactions based on data mining. In order to further verify the data accuracy of data mining technology, this paper analyzes the test values of neural network algorithm, seizure rate and inspection rate. When the misclassification loss parameter value is increased from 1 to 4, the detection rate of the model increases and the detection rate decreases. When the misclassification loss value is higher than 4, the detection rate increases, but the seizure rate decreases significantly. Therefore, the target inspection rate of the model can be changed by adjusting the misclassification loss parameter value of the model. If we set the parameter value of misclassification loss reasonably, we can achieve the goal of preventing and controlling the maximum risk with the lowest data mining cost. Through the analysis, the research in this paper has achieved ideal results, and made a contribution to data mining in cross-border e-commerce transaction risk data analysis.

In the era of rapid development of information, network marketing has gradually become one of the most popular marketing methods. But there are also some inevitable problems. The current network marketing is so aimless that people are bored with it and then marketing to the enterprise’s own brand, which makes the enterprise have to change the existing network marketing mode. Therefore, this paper takes big data as the background to study the innovation strategy of network marketing. This paper uses the form of questionnaire survey to investigate some consumers’ views and suggestions on online marketing in their daily life. According to the survey, big data can help marketers analyze consumer behavior preferences and market trends. In addition, this paper analyzes and introduces some problems existing in the traditional network marketing mode, and gives the network marketing strategy under the background of big data.

In recent years, Cross-border(CB) e-commerce(EC) has developed rapidly in our country, and the government has also introduced a series of measures to promote the development of CB EC. In the era of big data, recommendation systems are widely used in CB EC platforms, and more and more attention is paid to its personalized recommendation technology. Based on this, this paper studies the application of data mining algorithms in the construction of CB EC courses, which has certain guiding significance for the current market and enterprise development. By taking two CB EC companies as the research object, this paper uses data mining algorithms to mine and analyze the relevant data of these two EC companies, and find that a platform with a data mining system has more advantages in researching customer shopping preferences;comparing people’s satisfaction with EC companies with data mining systems and traditional companies, 83% of the participants in the experimental group were satisfied, of which 60% were very satisfied, while only 59% in the control group of people are satisfied.This shows that data mining algorithms are of great significance to the development of CB EC, and it also points out a new direction for the construction of CB EC courses, such as CB EC based on data mining algorithms. On the one hand, this will also play a positive role in the future development of CB EC.

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With the development of the times, cross-border e-commerce has gradually become an important course, but the current teaching methods are difficult to provide students with good teaching. Based on the background of big data, this article hopes to practice cross-border e-commerce in schools The teaching finds a practical form with good feasibility, and summarizes from it that can reflect the universally applicable internal laws, so that the school’s e-commerce teaching can make more students realize the role of big data. Studies have shown that cross-border e-commerce practice teaching under the background of big data is more popular among students than the current traditional teaching model, with a favorable rate of over 90%. And in terms of teaching efficiency, the efficiency of teaching based on big data reaches 0.835, which is much higher than traditional teaching methods. This shows that practical teaching of cross-border e-commerce based on big data can play an important role.