Artificial intelligence (AI) has become a transformative force across industries, with education and employment services increasingly adopting intelligent solutions to enhance efficiency and quality. In higher vocational education, the growing need for accurate internship placement and effective employment support underscores the value of integrating AI into institutional practices. Vocational colleges offer a valuable context to examine how data-driven systems can optimize the transition from education to the workplace. This paper investigates the application of AI within the National Smart Platform for Internship and Employment at Shanghai Vocational College of Science and Technology. Methodologically, it employs RealMLP with strong meta-tuned default parameters, a modern machine learning framework tailored for structured tabular data, to analyze survey responses from graduates using the platform. RealMLP balances efficiency and accuracy, while the meta-tuned defaults reduce the need for extensive hyperparameter tuning, making it practical for large-scale educational datasets. Results show that this approach effectively identifies the key factors influencing graduates' satisfaction, employment outcomes, and platform perceptions. More broadly, the analysis highlights which dimensions of the intelligent employment system most significantly affect students' overall experiences. By uncovering these patterns, the study provides evidence-based insights for understanding the impact of AI-driven analytics on vocational college internship and employment services. © 2025 Copyright held by the owner/author(s).
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