Application Of Data Mining Technology In Agricultural Statistics Analysis Of Hunan Province

Agriculture is the foundation of the national economy,and the development of agriculture directly affects the overall development of the national economy.The agricultural statistical survey is the most direct and comprehensive survey of important national conditions and national strengths to understand the development and changes of the “three rural”issues.Agricultural statistics will grasp the current status of agricultural,rural and peasant development in the country,understand the new levels of agricultural output levels,peasant living standards,new agricultural management entities,agricultural modernization,agricultural scale and industrialization,and fully demonstrate rural development and peasant life.The new situation is of great significance to the formulation and implementation of the ”three rural” policy,ensuring national food security,accelerating the realization of China's agricultural modernization and scale,new urbanization,and building a well-off society in an all-round way.Agricultural statistics show the current development status of “agriculture,rural areas and farmers”,and provide important data support for studying the changes of“agriculture,rural areas and farmers”and exploring the strategic deployment of rural revitalization.Based on the agricultural statistics of Hunan Province,this paper mainly does the following work:The first chapter mainly introduces the research background and significance of agricultural statistics in Hunan Province,and explains the current situation and existing problems of agricultural development in Hunan Province,and then expounds the research methods and ideas of this paper?The second chapter analyzes the degree of rural development in Hunan Province,next,a time series model for the per capita disposable income of rural residents in Hunan Province from 1997 to 2016 was constructed.On the basis of the third-order difference sequence,the model passes the stationarity test,and the model parameters are selected from the autocorrelation and partial autocorrelation coefficient graphs.Then the ARIMA(3,3,1)model is successfully constructed by the residual sequence correlation test.And use this model to predict the per capita disposable income of rural residents in Hunan Province from 2017 to 2020.The prediction results show that the relative error of the model prediction results in 2017 and 2018 is below 5%,and the model fitting effect is better.The third chapter analyzes the correlation of agricultural statistical data.It can be seen through the correlation degree visualization that the correlation between variables is high,and then the factor analysis of the data is carried out.Seven common factors are extracted by the principal component analysis variance cumulative interpretation rate.The weighted summation of the seven common factors is used to obtain the comprehensive strength ranking of rural development in various cities and states in Hunan Province.Next,in order to explore the regional differences between cities and states,clustered analysis and processing of the processed data,clustering the cities and states of Hunan Province into six categories,the clustering results are consistent with the actual situation,indicating that the clustering results are ideal.The fourth chapter summarizes the results of data analysis,and obtains some conclusions from related parties,which provides partial theoretical support for the implementation of rural revitalization strategy in Hunan Province.