Research on the Prediction and Intervention Model of Mental Health for Normal College Students Based on Machine Learning
Keywords:
Mental Health, Normal College, Deep Learning, Clustering, Feature Extraction, Markov ModelAbstract
This research paper investigates the utilization of fuzzy recognition technology in the analysis of mental health among normal college students, aiming to enhance the efficacy of packaging design for normal college institutions. The study's methodology comprises several essential steps to systematically analyze and comprehend the artistic and aesthetic attributes within mental health representations among the normal college. The proposed model combines Direction Point Cluster (DPC) segmentation techniques with fuzzy recognition algorithms, enabling accurate feature extraction and selection from mental health datasets. With the Random Probabilistic Markov Model (RPMM) for feature selection, the research harnesses the power of fuzzy logic and image processing to explore the nuanced attributes of normal college students' mental health. The integration of fuzzy recognition technology provides a means to manage inherent uncertainty and variability within mental health expressions. The findings of this study demonstrate the potential of fuzzy recognition technology in enhancing the analysis of normal college students' mental health. The RPMM model introduces a comprehensive framework for systematically assessing the aesthetic attributes of mental health representations. This integration opens avenues for creating more effective and visually captivating designs that align with cultural identity and aesthetic preferences. The paper concludes by outlining the step-by-step process of the RPMM model, which involves data collection, preprocessing, DPC segmentation, feature extraction, feature selection through RPMM, application of fuzzy recognition algorithms, and subsequent analysis. Additionally, the Direction Point Cluster (DPC) segmentation technique is introduced, presenting its role in capturing significant structural elements within mental health artworks.
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