Research on Efficiency Improvement and Empirical Study of Educational Resource Allocation Based on Improved Machine Learning Algorithm

Balanced and precise educational resource allocation is a core issue in advancing educational equity and improving overall quality. To address this problem, this paper proposes an optimized educational resource allocation model based on an improved random forest algorithm. First, theories related to educational resource allocation and machine learning are reviewed, and a core indicator system affecting resource allocation efficiency is clarified. Second, to address bias in feature weight allocation in traditional random forest algorithms, an adaptive weighting mechanism and cross-validation optimization strategy are introduced to construct an improved random forest algorithm. Third, basic educational resource data are used as samples, and the improved algorithm is applied to predict and optimize resource allocation efficiency. Comparative experiments are conducted to verify the model’s superiority. Finally, targeted suggestions for improving allocation efficiency are proposed based on empirical results. The results show that the R2 value of the improved random forest algorithm is 5.3 percentage points higher than that of the traditional algorithm, increasing from 0.873 to 0.926, with a 12.3% reduction in RMSE. The method effectively identifies key bottlenecks and supports precise resource optimization.