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Based on survey data from 384 university students, this study employed automated machine learning methods to construct a predictive model of subjective well-being, in which the Support Vector Machine (SVM) model performed best, achieving an overall prediction accuracy of 81.81%. The results indicate that the overall subjective well-being of the current university student population is relatively high; depression and interpersonal sensitivity are the most significant influencing factors, followed by hostility and paranoid ideation, among others. Family emotion also has a significant impact on subjective well-being. 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