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<p>In intelligent education, accurate prediction of college students’ psychological states is crucial for teaching quality improvement and mental health interventions. Psychological state data with temporal dependency and nonlinearity limits traditional machine learning, while Long Short-Term Memory (LSTM) is sensitive to hyperparameters and prone to premature convergence. To solve these problems, this paper proposes a CNWOA-LSTM framework combining an improved Whale Optimization Algorithm (WOA) with LSTM: chaotic initialization is introduced to enhance population diversity and global search ability, and a niche operator is added to alleviate premature convergence; the improved WOA optimizes key LSTM hyperparameters (hidden units, learning rate, dropout rate, batch size). Experiments are conducted on a public kaggle dataset of college students’ behavioral features, learning interaction data and psychological assessment indicators. CNWOA-LSTM is compared with traditional machine learning models (SVM, Random Forest, GBDT), classical deep learning models (CNN, Transformer, GRU, BiLSTM), and meta-heuristic optimized LSTM models (DE-LSTM, HHO-LSTM, MPA-LSTM, AOA-LSTM, RSA-LSTM). Results show CNWOA-LSTM achieves 93.64% accuracy, 9.33% and 6.63% higher than original LSTM and standard WOA-LSTM respectively, and 5.21%–7.34% higher than other meta-heuristic optimized LSTM models, verifying its effectiveness for psychological state prediction.</p>

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Prediction of college student psychological state based on deep learning framework combining the improved Whale Optimization Algorithm and LSTM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Xiaohan Sun,
  • Hanhui Liu

摘要

In intelligent education, accurate prediction of college students’ psychological states is crucial for teaching quality improvement and mental health interventions. Psychological state data with temporal dependency and nonlinearity limits traditional machine learning, while Long Short-Term Memory (LSTM) is sensitive to hyperparameters and prone to premature convergence. To solve these problems, this paper proposes a CNWOA-LSTM framework combining an improved Whale Optimization Algorithm (WOA) with LSTM: chaotic initialization is introduced to enhance population diversity and global search ability, and a niche operator is added to alleviate premature convergence; the improved WOA optimizes key LSTM hyperparameters (hidden units, learning rate, dropout rate, batch size). Experiments are conducted on a public kaggle dataset of college students’ behavioral features, learning interaction data and psychological assessment indicators. CNWOA-LSTM is compared with traditional machine learning models (SVM, Random Forest, GBDT), classical deep learning models (CNN, Transformer, GRU, BiLSTM), and meta-heuristic optimized LSTM models (DE-LSTM, HHO-LSTM, MPA-LSTM, AOA-LSTM, RSA-LSTM). Results show CNWOA-LSTM achieves 93.64% accuracy, 9.33% and 6.63% higher than original LSTM and standard WOA-LSTM respectively, and 5.21%–7.34% higher than other meta-heuristic optimized LSTM models, verifying its effectiveness for psychological state prediction.