Research on the Influencing Factors of Commercial Pension Insurance for Rural Residents in the Context of Population Aging Based on Big Data Analysis

Abstract

Data Pension Explorer (DAPE) introduces an innovative approach to delve into the intricacies of commercial pension insurance adoption within rural communities in China, particularly amid the challenges posed by an aging population. Leveraging advanced Deep Long Short-Term Memory (LSTM) techniques within the domain of big data analysis, this study pioneers the analysis of extensive datasets. It systematically unravels intricate patterns, correlations, and pivotal determinants shaping the landscape of pension insurance adoption in rural areas. Going beyond conventional analyses, this research provides a nuanced understanding of the multifaceted factors influencing both the sustainability and uptake of pension insurance. The culmination of these efforts yields valuable insights that extend beyond the theoretical realm, directly informing strategic decision-making processes. These insights prove instrumental in designing and implementing policies tailored to address the unique challenges faced by rural communities in China. Thus, DAPE not only navigates the complexities of population aging but also serves as a guiding force in fostering widespread adoption and sustainability of pension insurance within rural landscapes in the Chinese context.