Construction of a Comprehensive Risk Assessment Framework for Mental Health Education Based on Big Data Prediction and Neural Networks

A risk classification framework for student mental health is proposed, combining Principal Component Analysis (PCA), Z-score normalization, and a Seven-Spot Ladybird-Tuned Dynamic Elman Recurrent Neural Network (SSL-DERNN). Model hyperparameters were tuned using a Seven-Spot Ladybird Optimization (SSLO) metaheuristic. The proposed framework combines the big data analytics with a Seven-Spot Ladybird-Tuned Dynamic Elman Recurrent Neural Network (SSL-DERNN) to handle big, heterogeneous educational and psychological data effectively. The combination of meta-heuristic tuning and dynamic recurrent learning allows learning features robustly and predicting mental-health risks over large groups of students. Experiments used stratified 5-fold cross-validation; reported metrics are mean ±95% confidence intervals across folds. SSL-DERNN achieved accuracy 97.0% ( 95% CI: 96.1-97.9%), precision 95.5% ( 95% CI: 94.2 − 96.8% ), recall 93.2% ( 95% CI: 91.6 − 94.8%) and F1-score 92.6%(95% CI: 91.0-94.2%), significantly outperforming ANN, RF, RF-ANN, LSTM, GRU and BiLSTM baselines (paired tests, p < 0.01 ). Ablation analysis confirmed contributions of PCA and SSLO. Hardware, software, and reproducible hyperparameters are reported to aid replication.