基于多因素农业相关性分析及预测研究
Research on Multi-Factor Agricultural Correlation Analysis and Prediction

农业作为人类生存与发展的基础产业，其发展受到多种因素的影响。本文通过选取1999年至2019年我国31个地区的农业总产值、粮食产量、农业机械总动力、化肥施用量和农村用电量等因素，建立了基于多因素农业相关性分析及预测模型。首先，利用Z-score数据标准化法对原始数据进行标准化以获取初始分析数据；然后，建立了基于Pearson相关系数的农业相关性分析模型，将农业总产值、粮食产量、农业机械总动力、化肥施用量和农村用电量之间的关系分为7组极高相关关系和3组高度相关关系，并基于该分析结果对农业发展提出合理性建议；最后，建立了基于多项式拟合的农业总产值预测模型，得到农业总产值随时间变化的拟合函数为：$f\left(x\right)=63.9226x^2+1573.0684x+8308.2916$ ，并对2020~2023年农业总产值进行了预测，经检验其预测误差率低于3%，表明所建立的预测模型精度高，鲁棒性好。
Agriculture, as a fundamental industry for human survival and development, is influenced by multiple factors. This study selected agricultural data from 31 regions in China between 1999 to 2019, including total agricultural output value, grain yield, total agricultural machinery power, chemical fertilizer application amount, and rural electricity consumption. A multi-factor agricultural correlation analysis and prediction model was constructed. Firstly, the Z-score data standardization method was applied to preprocess raw data for initial analysis. Subsequently, an agricultural correlation analysis model based on the Pearson correlation coefficient was established. This model identified 7 sets of extremely high correlations and 3 sets of highly significant correlations among the five factors. Based on these findings, practical recommendations for agricultural development were proposed. Finally, a prediction model based on polynomial fitting for agricultural output value was developed. The fitting function was determined as follows: $f\left(x\right)=63.9226x^2+1573.0684x+8308.2916$ . Predictions for agricultural output values from 2020 to 2023 demonstrated high accuracy, with