【摘要】 为深入探究草地生态形成原因，基于草地生态足迹模型，利用K-means聚类算法对草地生态与关联指标进行层次识别，数据挖掘Apriori改进算法对草地生态进行成因分析。实验结果表明：改进后的算法在效率和有效性上都得到了提升；解译关联规则得出，经济因素方面，人均GDP的增长和就业人员第三产业占比的扩大刺激畜牧业对草地资源的消耗，在社会及自然因素方面，农牧区人口数量多、降雨量少、地理位置的差异都会对草地生态产生抑制作用。更多还原

【Abstract】 To further explore the causes of grassland ecological formation,based on the grassland ecological footprint model,the K-means clustering algorithm is used to hierarchically identify grassland ecology and related indicators,and the Apriori improved algorithm is used for data mining to analyze the causes of grassland ecology. The experimental results show that the improved algorithm has improved both efficiency and effectiveness. According to the interpretation of association rules,in terms of economic factors,the growth of per capita GDP and the expansion of the proportion of employment in the tertiary industry stimulate the consumption of grassland resources by animal husbandry.In terms of social and natural factors,the large population,low rainfall,and geographical differences in agricultural and pastoral areas can all have inhibitory effects on grassland ecology.更多还原