Exploring Families' Perception of Adolescent Mental Health: Using AI to Strengthen Government Social Risk Resilience

This study explores the current state of family mental health awareness among adolescents and its promotion pathways, proposing optimization strategies by integrating artificial intelligence (AI) technology. Based on literature analysis, the research highlights the growing severity of adolescent mental health issues and the critical role of family factors in prevention and intervention. However, existing studies primarily focus on psychological perspectives, lacking economic and management viewpoints, while AI applications mainly target post-hoc interventions rather than daily prevention. Using a mixed-methods design, the study developed the Family Adolescent Mental Health Awareness Questionnaire covering three dimensions: emotional management, behavioral support, and social relationships. Exploratory and confirmatory factor analyses validated its reliability and validity. An empirical survey of 295 parents in Beijing revealed that most possess basic awareness in emotional management and behavioral support but struggle with complex emotion recognition and sudden emotion coping. Notably, 30% of parents did not consider seeking professional help for adolescent depressive symptoms. Gender difference analysis showed significantly higher support willingness among female parents. The research further proposes a three-stage AI application framework: 1) privacy-protected multi-source data collection via blockchain encryption; 2) development of natural language processing-based intelligent chatbots for personalized mental health support; 3) establishment of an online education and early-warning system integrating families, schools, and governments to strengthen early intervention. The marginal contribution lies in optimizing mental health service systems through digital technology, providing data-driven policy recommendations for policymakers, and offering actionable solutions for family awareness improvement. Future research should expand sample geographical representation and explore technological adaptability across diverse scenarios.