Quality Evaluation of Higher Vocational Education based on Adam Optimized Hybrid RF+BP Neural Network

The evolving demands of higher vocational education necessitate advanced assessment frameworks to address the limitations of traditional methods, such as rigidity and insufficient granularity. This study proposes a hybrid Random Forest (RF) and BP Neural Network (NN) model, optimized with adaptive momentum (Adam) and dropout regularization, to dynamically evaluate education quality using multi-source data, including student performance, institutional resources, and employment outcomes. The RF algorithm first selects key features, enhancing interpretability, while the BPNN leverages error backpropagation and gradient descent to autonomously weight critical indicators. Experimental results demonstrate the hybrid model’s superior performance, achieving 96.1% accuracy (RMSE = 0.15), a 3-5% improvement over standalone BPNN and traditional methods (ANOVA, PCA). Robustness tests confirm its stability across diverse datasets, with performance metrics (MAE, R², F1-score) validating its effectiveness in capturing non-linear relationships, such as curriculum-industry alignment. Tested across five regions, the framework enables precise policy decisions, optimizing resource allocation and accreditation to ensure vocational training meets labor-market needs. This approach offers a scalable, data-driven solution for enhancing higher vocational education quality.