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

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.