A Hippopotamus Optimization Algorithm-Based Convolutional Neural Network Model for Mental Health Assessment Among College Students

The mental health of adult students is crucial not only for enhancing their learning experience and overall quality of life, but also for alleviating academic and employment-related anxiety. A significant challenge in developing effective online mental health support systems is the accurate assessment of students' mental health status. Current evaluation methods often lack precision and fail to integrate multifaceted data perspectives. To address these challenges, this study developed a psychological assessment system based on deep learning technology. The system aims to assess adult students' psychological states and provide appropriate support. Specifically, it utilizes a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) algorithm framework to evaluate students' psychological states by synthesizing image data, academic performance, and textual inputs. Furthermore, to enhance the accuracy of deep learning-based mental health assessment models, an improved hippopotamus optimization (IHO) algorithm was designed to optimize the hyperparameters of deep learning frameworks. By using the proposed multi-input single-output hybrid IHO-based LSTM-CNN framework (IHO-LSTM-CNN), the online mental health assessment module can accurately describe the psychological status of college students and provide personalized support to meet their specific needs. The final results indicate that the IHO-LSTM-CNN framework provides more accurate assessments than existing mental health assessment models, with an accuracy of 90.28%. This enhanced accuracy enables online community psychological support systems to deliver precise and effective psychological support to college students.