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Early identification and intervention are crucial to preventing these problems from worsening and improving students\u2019 psychological resilience. However, existing prediction models often fail to handle the complexity and dynamic nature of mental health data. These models struggle to capture both the spatial and temporal features of psychological conditions and are further limited by challenges related to data quality, such as noise, imbalance and diversity. To address these limitations, this study proposes a hybrid prediction model based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs). Specifically, CNN is employed to extract spatial features from mental health data, identifying underlying patterns, while GRU is used to model temporal dependencies and capture dynamic changes in psychological states. By integrating these two approaches, the CNN\u2013GRU model effectively combines spatial and temporal feature extraction, providing a comprehensive solution to the challenges of mental health prediction. Experimental results show that the CNN\u2013GRU model surpasses traditional models in accuracy, precision and recall, effectively detecting complex patterns in mental health data. This study offers a reliable tool for early mental health issue detection and intervention, while also providing useful insights for future deep learning applications in mental health research.<\/jats:p>","DOI":"10.1142\/s0218126625504328","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T17:02:14Z","timestamp":1753894934000},"source":"Crossref","is-referenced-by-count":1,"title":["Hybrid CNN\u2013GRU Model for Early Detection and Prediction of Mental Health Issues"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6037-2025","authenticated-orcid":false,"given":"Lisha","family":"Ying","sequence":"first","affiliation":[{"name":"Rural Education Revitalization College, Shangrao Preschool Education College, Shangrao 334000, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8395-3287","authenticated-orcid":false,"given":"Yujun","family":"Wang","sequence":"additional","affiliation":[{"name":"Ministry of Public Basic Education, Shangrao Preschool Education College, Shangrao 334000, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"S0218126625504328BIB001","first-page":"9970363","volume":"2022","author":"Chung J.","year":"2022","journal-title":"Appl. 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