中国农业新质生产力发展水平测度与影响因素分析OA

The development of agricultural new quality productivity plays an important role in promot-ing the modernization of China's agriculture and achieving the strategic goal of a strong agricultural country.In order to objectively quantify the nonlinear effects and importance of key factors affecting the level of agricultural new quality productivity.The article aims to propose a framework for measuring and analyzing the development level of agricultural new quality productivity based on machine learning methods.The Extreme Gradient Boosting(XGBoost)algorithm,SHAP machine learning interpreta-tion method and TOPSIS model are utilized to measure and analyze the development level of agricul-tural new quality productivity in China from 2012 to 2022.In addition,five-fold cross-validation is ap-plied to test the robustness of the machine learning regression model results.Finally,the SHAP model is used to deeply analyze the key driving factors affecting the level of China's agricultural new quality productivity and explore the path to promote the development of China's agricultural new qual-ity productivity.The results of the study show that:the overall level of China's agricultural new quality productivity level is on an upward trend,but the overall level is low;scientific and technological inno-vation talents,the scale of development of high-tech industry and the level of development of the digi-tal economy are the key driving factors affecting the level of development of China's agricultural new quality productivity,and they have a significant positive effect and non-linear characteristics.