Cross‐border trade is also changing and innovating. Electronic payment has become the core application of modern cross‐border e‐commerce. However, although electronic payment has brought convenience and efficiency to enterprises and social life, there are also many problems in transaction information security. These problems not only hinder the development of electronic payment but also bring hidden dangers to people’s property security. Therefore, an encryption algorithm of e‐commerce was proposed, and its algorithm security and risk control mode was further studied by means of case analysis, empirical analysis, and comparative analysis. Experimental data show that blockchain technology is a breakthrough. The aim of the study is to explore its application in specific circumstances and strive to make the new e‐payment mode in line with international standards.

The powerful advanced manufacturing industry is the most powerful driving force for economic development and growth, and it is also the main source of environmental pollution. Artificial intelligence and blockchain technology are recognized as a breakthrough technology that can be widely used, changing the way the entire society and economy operate. The main constraints affecting the sustainable development of the manufacturing industry are ecological deterioration and resource shortage, but the development of artificial intelligence and blockchain technology provides new ideas for solving manufacturing problems. Based on this, this paper proposes a research on the sustainable development performance of green manufacturing technology innovation based on artificial intelligence and blockchain technology. This paper deeply grasps the essence and connotation of artificial intelligence and blockchain technology, analyzes its specific application form and research background, searches for the effective effect of manufacturing technology innovation and green manufacturing performance path, and clarifies the mechanism between the two. All measurement items in the questionnaire used in this article use Likert5 scale and use 15 options to indicate the degree of conformity with the actual situation of the enterprise. The results show that the average green manufacturing capacity of each measurement item is between 3.183.97, indicating that the company's green manufacturing capacity is relatively high. Among them, the ability of green technology innovation is relatively high, indicating that most companies have noticed that the improvement of the ability of green technology innovation plays a vital role in the future sustainable development of enterprises. Moreover, more and more companies are paying attention to the latest applications of blockchain technology, which can effectively promote the development of enterprises.

With the development of technology, the data stored by humans is growing geometrically. Especially in the logistics industry, the rise of online e-commerce has created a huge data flow in the informatized logistics network. How to collect, analyze, and organize this information in time and analyze the meaning of this information from it is a difficult problem. The paper aims to learn the management of logistics systems from the perspective of statistics. This article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. The information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. The experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits. The experimental data show that mining effective and accurate information from massive information can help companies to quickly make decisions, formulate scientific logistics management programs, improve operating efficiency, reduce operating costs, and obtain good benefits.

With the birth of the Internet, information and data can flow in both directions, and, after that, it has spread to thousands of households. On this basis, e-commerce was also born, and it took a very short time from the field that no one cared about at the beginning to the hot focus field now. The development of e-commerce has broadened the sales channels of farmers, which has further increased the sales benefits of fruits and other agricultural products, conformed to the development of the times, and further supplemented the sales of fruits and other agricultural products. Based on the use of embedded microprocessors, this paper constructs an e-commerce system integrating sales and logistics. Based on the logistics model in the e-commerce platform, an e-commerce logistics identification model is constructed to measure the e-commerce logistics service level of agricultural products such as fruits. The article analyzes the sales and transportation methods of fruits and other agricultural products. Through a detailed analysis of the traditional e-commerce system, the e-commerce sales system has been optimized and improved by combining the reference cases and the data obtained from the Internet, especially in the aspect of logistics transportation. According to the data experiments in this article, it is shown that, after using the data formula for calculation, the best plan for transporting fruits and other agricultural products can be obtained. In the calculation result of the formula, the cost of delivery without considering customer satisfaction is 586 yuan, and the average delivery time of the vehicle is 5.625 hours, but the average satisfaction is only 78.5%. Compared with customer satisfaction, although the cost is about 15 yuan more and the delivery time is a few minutes longer, the customer satisfaction is about 21% higher than the customer satisfaction without considering the customer. Based on the optimized and transformed e-commerce system, this article will further improve the e-commerce platform for fruits and other agricultural products.

With the rapid development and application of internet technology, people are gradually entering a state of digital life. However, it is difficult for digital assets to be circulated with a unified standard protocol. Blockchain has the characteristics of decentralization, trustworthiness, and immutability, which can be used to solve the above problems. Although virtual marketing technology shows great potential, as a form of organization that is difficult to manage, there is still a lack of experience in the form factors and operation mechanism of a virtual business model. Therefore, combined with the realistic background, this paper built a digital twin innovation and entrepreneurship virtual marketing interactive platform based on blockchain. The article analyzed the data structure of the blockchain and completed the construction of the platform operation mode. It studied the encryption and decryption process of cryptography and expanded the ciphertext space, then the blockchain is encrypted and decrypted again, and finally, a chi-square analysis is performed. After building the platform, it conducted market research and analyzed the main operating indicators of the A platform on the market. Then, it compared the uncertain string preprocessing enumeration length change and the index occupied space between the platform constructed in this paper and the platform in the literature. The comparison results have shown that the index proposed in this paper is 320 MB in 8 iterative calculations, while the bibliographic index starts at 10000 MB and keeps increasing during the iterative calculation process. The two are not in the same order of magnitude, illustrating the superiority of the platform in this paper.

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.