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<p>With the implementation of a rural revitalization strategy, the management of the rural industrial economy urgently needs further innovation and optimization. In this paper, principal component analysis is used to identify the key factors of the problems of agricultural and industrial economic management in the context of rural revitalization, and potential indicator factors are uncovered through principal component analysis, and these potential factors are used instead of all indicators for more accurate analysis. Meanwhile, the PCA method was combined with a support vector machine to construct a PCA-SVM model, based on which the influence factors of agricultural economic efficiency were measured. The higher the degree of informationization of agricultural management, the greater the influence factor on agricultural economic benefits, and the correlation rate reached 26.85%. The higher the degree of the system construction of agricultural management, the greater the influence factor on agricultural economic efficiency, and the correlation rate reached 32.14%. The higher the degree of improvement of infrastructure construction, the higher the influence factor on agricultural economic efficiency, with a correlation rate of 35.68%. This study can precisely analyze the current problems in the economic management of the agricultural industry and provide effective references for promoting the economic development of rural areas in the context of rural revitalization.</p>
</abstract>

With the implementation of a rural revitalization strategy, the management of the rural industrial economy urgently needs further innovation and optimization. In this paper, principal component analysis is used to identify the key factors of the problems of agricultural and industrial economic management in the context of rural revitalization, and potential indicator factors are uncovered through principal component analysis, and these potential factors are used instead of all indicators for more accurate analysis. Meanwhile, the PCA method was combined with a support vector machine to construct a PCA-SVM model, based on which the influence factors of agricultural economic efficiency were measured. The higher the degree of informationization of agricultural management, the greater the influence factor on agricultural economic benefits, and the correlation rate reached 26.85%. The higher the degree of the system construction of agricultural management, the greater the influence factor on agricultural economic efficiency, and the correlation rate reached 32.14%. The higher the degree of improvement of infrastructure construction, the higher the influence factor on agricultural economic efficiency, with a correlation rate of 35.68%. This study can precisely analyze the current problems in the economic management of the agricultural industry and provide effective references for promoting the economic development of rural areas in the context of rural revitalization.

Study on the optimization strategy of agricultural industry economic management innovation in the context of rural revitalization in the multi-Internet era

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the implementation of a rural revitalization strategy, the management of the rural industrial economy urgently needs further innovation and optimization....

Study on the optimization strategy of agricultural industry economic management innovation in the context of rural revitalization in the multi-Internet era

Pubblicato online: 09 set 2023

Pagine: -

Ricevuto: 27 nov 2022

Accettato: 20 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00301

Parole chiave
Principal component analysis, Support vector machine, Rural revitalization, Agricultural economic management, PCA-SVM model

© 2023 Lili Guan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Study on the optimization strategy of agricultural industry economic management innovation in the context of rural revitalization in the multi-Internet era

Pubblicato online: 09 set 2023

Pagine: -

Ricevuto: 27 nov 2022

Accettato: 20 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00301

Parole chiavePrincipal component analysis, Support vector machine, Rural revitalization, Agricultural economic management, PCA-SVM model

© 2023 Lili Guan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Principal component analysis, Support vector machine, Rural revitalization, Agricultural economic management, PCA-SVM model