摘要
新质生产力是我国经济高质量发展的新动能.针对新质生产力发展水平的不同测度方法,文章基于随机森林回归构造了一种统计测度选择模型,借助新质生产力发展水平数据对选择模型进行验证和分析,给出了熵权法、熵权TOPSIS法和熵权CRITIC法的有效选择标准.结果显示,在对新质生产力发展水平进行测度时,熵权TOPSIS法优于另外两种测度方法.基于此,分析了2010-2022年我国各地区新质生产力发展水平的时空演进特征.
New quality productivity is a new driving force for the high-quality development of China's economy.This paper constructs a statistical measurement selection model based on random forest regression for different measurement methods of the development level of new quality productivity.The selection model is validated and analyzed using data on the development level of new quality productivity.Effective selection criteria for entropy weight method,entropy weight TOPSIS method,and entropy weight CRITIC method are provided.The results show that when measuring the development level of new quality productivity,entropy weight TOPSIS method is superior to the other two measurement methods.Based on this,the spatiotemporal evolution characteristics of the development level of new quality productivity in various regions of China from 2010 to 2022 were analyzed.
作者
杨彦炯
YANG Yan-jiong(Department of Mathematics,Taizhou University,Taizhou 225300,China)
出处
《数学的实践与认识》
北大核心
2025年第1期57-67,共11页
Mathematics in Practice and Theory
基金
江苏省青蓝工程项目(2022)。
关键词
新质生产力
随机森林回归
熵权法
选择模型
new quality productivity
random forest regression
entropy weight method
selection model
