Global concern over food prices and security has been exacerbated by the impacts of armed conflicts such as the Russia Ukraine War, pandemic diseases, and climate change. Traditionally, analyzing global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors and their implications extend beyond simple linear relationships. By incorporating determinants, critical characteristics identification, and comparative model analysis, this study aimed to identify the critical socioeconomic characteristics and multidimensional relationships associated with the underlying factors of food prices and security. Machine learning tools were used to uncover the socioeconomic factors influencing global food prices from 2000 to 2022. A total of 105 key variables from the World Development Indicators and the Food and Agriculture Organization of the United Nations were selected. Machine learning identified four key dimensions of food price security: economic and population metrics, military spending, health spending, and environmental factors. The top 30 determinants were selected for feature extraction using data mining. The efficiency of the support vector regression model allowed for precise prediction making and correlation analysis. Keywords: environment and growth, global economics, price fluctuation, support vector regression

翻译：全球对粮食价格及安全的关切因武装冲突（如俄乌战争）、流行病和气候变化等影响而加剧。传统上，分析全球粮食价格及其与社会经济因素的关联依赖于静态线性回归模型。然而，社会经济因素的复杂性及其影响远超简单线性关系。通过整合决定因素、关键特征识别及模型比较分析，本研究旨在识别与粮食价格及安全基础因素相关的关键社会经济特征和多维关系。采用机器学习工具揭示2000年至2022年间影响全球粮食价格的社会经济因素。共选取世界发展指标及联合国粮农组织中的105个关键变量。机器学习识别出粮食价格安全的四个关键维度：经济与人口指标、军费支出、医疗支出及环境因素。通过数据挖掘提取前30个决定因素进行特征提取。支持向量回归模型的高效性使其能够实现精确预测与相关性分析。关键词：环境与增长、全球经济、价格波动、支持向量回归