Journal of Information Organization Ezendu Ariwa 16 1 2026 https://doi.org/10.6025/jio/2026/16/1/1-12 https://www.dline.info/jio/fulltext/v16n1/jiov16n1_1.pdf This study addresses the critical gap in reliable, scalable assessment of college students innovation and entrepreneurship capabilities by developing a digital, data driven evaluation system. Leveraging social media analytics, data mining techniques, and the Analytic Hierarchy Process (AHP), the framework constructs a three tier hierarchical indicator structure encompassing personal qualities (innovative awareness, entrepreneurial motivation, psychological resilience), skill capabilities (professional knowledge, practical innovation, teamwork), and learning experiences (self-directed learning, project participation, entrepreneurial practice). Through crowdsourcing principles and frequency based indicator refinement, the system transforms qualitative competencies into quantifiable metrics organized within a five level classification (A–E). Experimental validation with 1,250 undergraduate students demonstrated that the proposed system exhibited superior psychometric properties relative to conventional approaches, achieving higher reliability (Cronbach's  = 0.857), operational efficiency (0.853), and structural validity (average factor loading = 0.275). The integrated platform deployed via cloud infrastructure with a WeChat miniprogram interface enables objective benchmarking, precise talent identification, and evidence based pedagogical interventions. By bridging digital youth engagement practices with rigorous multi criteria decision making, this research contributes a scalable, empirically validated instrument for educators and policymakers to systematically nurture innovation ready graduates. The framework’s robust performance supports its application in curriculum design, mentorship allocation, and resource optimization within entrepreneurship education ecosystems, ultimately advancing the development of future ready talent capable of driving socioeconomic transformation.