基于LSTM-GM模型的中国人口老龄化影响因素分析及趋势预测

Analysis of Influencing Factors and Trend Prediction of Population Aging in China Based on the LSTM-GM Model

摘要: 随着中国人口年龄结构的变化，人口老龄化已不可避免地成为了社会热点。为了深入研究中国人口老龄化现象，本文首先分析了中国人口老龄化的影响因素，从人口结构、经济发展、教育普及和医疗水平四个维度选取指标，运用灰色关联分析法和主成分分析法对影响中国人口老龄化的因素进行分析。其次，构建了LSTM-GM组合预测模型对人口老龄化系数进行实证分析，结果证明了LSTM-GM模型在中国人口老龄化预测中的有效性。

Abstract: With the shifting age structure of China’s population, population aging has inevitably emerged as a critical societal issue. To conduct an in-depth investigation into the phenomenon of population aging in China, this study first analyzes its influencing factors. Indicators are selected across four dimensions—population structure, economic development, education accessibility, and healthcare standards—followed by an analysis using the Grey Relational Analysis method and Principal Component Analysis. Subsequently, an LSTM-GM combined prediction model is constructed to empirically analyze the population aging coefficient. The results validate the effectiveness of the LSTM-GM model in predicting China’s population aging trends.