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.

With the advancement of electric vehicles, their low energy recovery efficiency has become the main obstacle to development. This study focuses on the problem of braking energy loss in electric vehicles during urban road driving and proposes an improved fuzzy control strategy to optimize the energy management of electric vehicles. The exploration first introduces fuzzy control logic to adjust and optimize the energy recovery system of electric vehicles and then introduces a sparrow search algorithm to optimize the adjustment parameters. Finally, using MATLAB R2022a simulation software environment, a comparative analysis is conducted on two driving cycles: urban dynamometer driving schedule and New York City conditions. Simulation results show that the improved fuzzy control strategy can recover 906.41 kJ of energy under urban driving cycle conditions, and the energy recovery rate reaches 49.00%, while the ADVISOR strategy is 507.47 kJ and 27.13%, respectively. The energy recovery rate of the research method is 21.87% higher than that of the comparison method. Improved energy recovery rate of 80.68%. In the driving cycle with New York City, the improved strategy recovered 294.45 kJ of energy, and the energy recovery rate was 48.54%. Compared with the ADVISOR strategy, the energy recovery rate increased by 100.20%, and the energy recovery rate increased by about 110.77%. The research results indicate that the improved fuzzy control strategy is significantly superior to the ADVISOR control strategy, effectively improving energy recovery efficiency and battery charge state maintenance ability under an urban dynamometer driving schedule, achieving more efficient energy management.

Following the World Health Organization’s 2023 declaration, which ended the global health emergency, energy policy shifted from a short-term crisis response to a structural recovery focused on renewable energy. However, the current literature remains fragmented, often overlooking the realities of implementation in the Global South and failing to integrate diverse policy instruments. This study examines post-pandemic renewable recovery strategies to categorize instruments, evaluate effectiveness, and identify critical implementation gaps. An integrative review was conducted, combining bibliometric mapping of 113 documents (n = 113) and systematic thematic synthesis of 42 studies (n = 42), utilizing the SPIDER and PRISMA protocols. Policy instruments were classified into five groups: Recovery (REC), Fiscal/Financial (FISC), Regulatory (REG), Energy Efficiency (EE), and Social and Information (SOC), revealing a “Global North-South Asymmetry”, where advanced economies leverage fiscal–regulatory coupling while emerging markets face administrative bottlenecks. Findings identify coordination failures, such as missequencing, and propose a “Cascading Policy Logic” that prioritizes de-risking before mandatory standardization. This research bridges the evidence gap by validating the need for informal sector mechanisms and equity safeguards in developing nations. Ultimately, this review provides a strategic framework for policymakers to transition from a reactive stimulus to durable, socially legitimate decarbonization pathways beyond 2025.

This study investigates the shifting role of digital platforms in enabling entrepreneurship in China's knowledge economy within the context of inclusivity, sustainability, and equity. Drawing on qualitative and quantitative methods, including multiview augmented reality, visualization/augmented reality focus groups, and regression analysis, the study assesses urban and rural entrepreneurial dynamics, the sustainability of crowdfunding models over time, and resource allocation across socioeconomic and geographical contexts through digital ethnography and focused area analysis. Results reveal considerable differences between urban and rural entrepreneurs, with urban customers profiting from advanced infrastructure and analytics tools. Conversely, rural entrepreneurs are impeded by a lack of internet connectivity and digital literacy. This paper adds to the understanding of network effects and resource allocation among platform ecosystems. It emphasizes the importance of creating inclusive policies and platform innovations.