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.

中文翻译：

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基于机器学习和大数据分析的在线购物顾客忠诚度统计建模与仿真

随着在线购物用户数量的增加，客户忠诚度与产品销售直接相关。本研究主要探讨基于机器学习和大数据分析的在线购物顾客忠诚度的统计建模和仿真。本研究主要使用机器学习聚类算法来模拟客户忠诚度。调用基于Hash结构的k均值交互式挖掘算法，对企业信用风险的多维层次树进行数据挖掘，根据特定要求不断调整不同数据挖掘级别的支持阈值，并选择有效的关联规则，直到获得满意的结果获得。对企业进行信用风险评估和预警建模后，获得初始的预选模型。网络爬虫首先从目标网站到临时网页数据库获取要收集的信息，该信息将通过一系列预处理步骤（例如完成，重复数据删除，分析和提取）来确保所抓取的网页已正确分析，以避免在爬网过程中由于网络错误而导致数据不正确。正确解析的数据将被存储，以进行下一步的数据清理或数据分析。要编写用于解析HTML文档的Java程序，请首先设置subject关键字和URL，然后通过分析网站的结构从获取的文件或字符串中解析HTML。其次，使用CSS选择器查找网页列表信息，检索数据，并将其存储在Elements中。在模型的整体拟合测试中，均方根误差近似值（RMSEA）值为0.053，介于0.05和0.08之间。结果表明，本研究设计的模型取得了较好的拟合效果，增强了顾客对购物网站的认知，关系信任在维护顾客忠诚度方面起着更大的作用。