The increasing complexity of modern labor markets and entrepreneurial ecosystems demands service systems that adapt to users' multidimensional profiles. Drawing on a systems approach and socio-technical theory, the author proposes a unified architecture for personalized career guidance and entrepreneurship training. A multilayer network encodes heterogeneous data—career behaviors, ability assessments, and interest embeddings—for unified feature representation. A multitask optimizer combining cross-entropy and weighted regression losses enables end-to-end training for career-direction recommendation and ability-growth-path prediction. The system comprises a real-time feature collection module, a model inference engine with feedback loops, and a closed-loop interface for multi-round dynamic recommendations. Experimental results on entrepreneurial project managers demonstrated 94.0% accuracy in career-direction recommendations, a mean squared error of 3.09 for ability-path prediction, and 89.5% top-10 course coverage.
Xiaoxia Xu (Wed,) studied this question.