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In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust.

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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.

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This study mainly aimed to make 3D printing technologies serve as the guidelines for the development of technology-oriented industries. The most important one was tasked to establish modeling technology applicable to 3D printing in view of technological development. For the substrate material of 3D printers, aside from commonly usable plastic, new carbon fiber composite substrates have been proposed. Substrates were selected for manufacturing dependent on different object characteristics. The components were manufactured mainly by focusing on small-sized aircraft components. Additionally, potential problems encountered during 3D printing were explored with feasible suggested solutions. In the aerospace industry, because of the extreme requirements for the weight reduction of aircraft components, in the past, this was limited by manufacturing difficulties. If specific shapes were required, it was highly difficult to produce a component in a single-cast production or cut from a single metal piece. Component manufacturing often had to be divided into several planning blocks, and then welding, assembly, or rivet connection was conducted. This situation was not only flawed with structural weaknesses but also extra weight. If metal powder was operable with 3D printing for integral molding, the above disadvantages could be avoided.

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.