A Predictive Model of the Impact of Government Subsidies on the Performance of Agricultural Enterprises Based on Machine Learning Algorithms

Huang Peihong; Li Jing; Liu Jiaxi

doi:10.54254/2755-2721/2025.25826

Applied and Computational EngineeringOpen access

A Predictive Model of the Impact of Government Subsidies on the Performance of Agricultural Enterprises Based on Machine Learning Algorithms

Research Article

Open Access

A Predictive Model of the Impact of Government Subsidies on the Performance of Agricultural Enterprises Based on Machine Learning Algorithms

Huang Peihong ¹, Li Jing ², Liu Jiaxi ^3*

¹ School of Economics & Management, Foshan Polytechnic, Guangdong, Foshan, 528137, China

² School of Economics & Management, Foshan Polytechnic, Guangdong, Foshan, 528137, China

³ School of Economics & Management, Foshan Polytechnic, Guangdong, Foshan, 528137, China

^*Corresponding author: caiguan2025@163.com

Published on 5 August 2025

ACE Vol.176

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-239-3

ISBN (Online): 978-1-80590-240-9

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Abstract

Scientifically assessing the true effect of government subsidies on the performance of agricultural enterprises and deeply revealing its mechanism of action and restrictive factors holds significant theoretical and practical value for optimizing agricultural policy design, enhancing the efficiency of fiscal resource allocation, and promoting high-quality agricultural development. To precisely analyze this impact, this study innovatively designed a Transformer classification algorithm that integrates adaptive feature interaction and adversarial robustness. Through empirical testing with four sets of comparison models including random forest, decision tree, XGBoost, and CatBoost, the results show that in the benchmark model, XGBoost has the best predictive performance with an accuracy rate of 85.3%. However, the new model proposed in this paper has achieved significant improvements in key performance indicators: accuracy (Accuracy) increased by 3.6%, recall (Recall) improved by 4.3%, precision (Precision) rose by 3.9%, and the F1 score increased by 4.1%. This research not only provides advanced analytical tools and empirical evidence for a deeper understanding of the complex relationship between government subsidies and the performance of agricultural enterprises but also offers important data support and decision-making references for precise policy implementation to enhance subsidy effectiveness and promote agricultural modernization.

Keywords:

Government subsidies, agricultural enterprise performance, machine learning.

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References

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References

[5]. Yao LJ, Pan J, Wang XZ, Chen YJ. A Forming Method for Al-Sc Alloy Target: CN201711302847.5 [P].2019-11-05.

[6]. Jia Q, Cao XM, Jiao XK, Li LL, Zhang XN, Hu HL, et al. A Low-Segregation Al-Sc Alloy Target and Its Preparation Method: CN202210514946.4 [P].2024-05-17.

[7]. Cao XM, Ding ZC, Li YJ, Jia Q, Li LL, Qu P, et al. A One-Step Forming Preparation Method for Diffusion-Welded AlSc Alloy Target: CN202011617741.6 [P].2022-06-17.

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Cite this article

Peihong,H.;Jing,L.;Jiaxi,L. (2025). A Predictive Model of the Impact of Government Subsidies on the Performance of Agricultural Enterprises Based on Machine Learning Algorithms. Applied and Computational Engineering,176,43-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Machine Learning and Automation

ISBN: 978-1-80590-239-3(Print) / 978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Conference website: 978-1-80590-240-9

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.176

ISSN: 2755-2721(Print) / 2755-273X(Online)

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