Jianing Deng, Ming Yu, Wenbo Yu, and Yuanchun ManThis email address is being protected from spambots. You need JavaScript enabled to view it.
Changchun University of Chinese Medicine, Jilin, Changchun, 130000, China
Received: October 4, 2025 Accepted: November 16, 2025 Publication Date: March 5, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
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) meta heuristic. 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.
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