Machine Learning-based Analysis of Chinese Population Structure

Abstract

Using in-depth data analyses and scientific methods, this paper provides an exhaustive discussion of the gender composition, urban-rural distribution, and age levels of the population in various provinces of China, thus revealing the far-reaching impact of the evolution of the population structure on social and economic progress. Firstly, using data visualisation techniques combined with K-mean cluster analysis, this study reveals the phenomenon of gender imbalance and proposes a series of countermeasures, including strengthening education on gender equality, promoting gender-equal social participation, and paying attention to gender-selective behaviours. Secondly, the study provides an in-depth analysis of the trend of population migration in the process of urbanisation and the problems derived therefrom, and advocates the government to adopt scientific planning and policy guidance measures in order to narrow the development gap between urban and rural areas, and to promote integrated urban and rural development. Finally, the paper provides a detailed analysis of the trend of population aging and insights into the challenges it poses to the pension service system, health care system and labour market, suggesting the formulation of a more flexible fertility policy and the vigorous development of the pension service industry to cope with the challenges posed by aging.