Artificial Intelligence-Driven Personalized Employment Guidance for College Students: Model Construction and Effectiveness Evaluation

Abstract

This paper proposes an AI-empowered personalized employment guidance system integrating multi-modal deep learning, graph neural network-based collaborative filtering, and knowledge graph enhancement within an adaptive multi-algorithm fusion framework, addressing the limitations of insufficient personalization and low matching accuracy in conventional approaches. The system introduces dynamic student profile modeling, temporal-aware student–position matching, and domain-adaptive feature weighting as core innovations. Experiments on 68,247 student samples and 172,456 job postings from 15 universities demonstrate that the system achieves 87.6% recommendation accuracy (+26.8% over traditional methods), outperforms the strongest baseline by 6.2% in AUC and 5.4% in NDCG@10 ( p <0.001), reduces job search duration by 38.7%, and improves employment success efficiency by 32.4%. These findings confirm the practical value of systematic AI integration for intelligent transformation of university career services.