Affiliations: School of Accountancy, Anyang Institute of Technology, Anyang, Henan 455000, China | E-mail: happyxiaoxin2022@163.com
Correspondence:
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Corresponding author: School of Accountancy, Anyang Institute of Technology, Anyang, Henan 455000, China. E-mail: happyxiaoxin2022@163.com.
Abstract: In the post-targeted poverty alleviation era, rural revitalization has become a common action of the whole society, strengthen the rural ecological environment governance, and the construction of beautiful countryside needs to be promoted urgently. Agricultural development, rural prosperity and farmers’ prosperity are inseparable from the support of a good ecological environment. From ecological, production, life and new energy four aspects of the rural ecological environment development evaluation index system, and then the principal component analysis screening important influence index, on the basis of the genetic algorithm and BP neural network improvement model, 31 provinces during much starker choices-and graver consequences-in rural ecological environment development, and the BP neural network and GA-BP neural network evaluation results. The results show that: (1) Generally speaking, during the 13th Five-Year Plan period, my country’s rural ecological environment development index has gradually improved, but the change range is small, the average value has risen from 0.2257 to 0.2431; The number of provinces with excellent development levels has risen from 5 to 7, and the development of rural ecological environment in Beijing, Tianjin and other provinces has risen to excellent; (2) The development of regional rural ecological environment has increased or decreased, and about three-quarters of the provinces have improved the development of rural ecological environment; (3) The development of rural ecological environment is uneven, and the difference gradually expands; (4) Compared with BP neural network, GA-BP neural network has fast convergence speed, small training, verification and overall errors, high fitting degree, and has a good evaluation effect. The research conclusions can provide a basis for the evaluation and improvement of rural ecological environment development.
Keywords: Rural ecological environment, principal component analysis, genetic algorithm, BP neural network, development evaluation
