Intelligent Training System for Future Skill Needs: A Method of Integrating Deep Learning and Skill Graph

With the rapid development of global industrialization and technology, the demand for skills in the labor market is constantly changing, and traditional vocational training methods are no longer able to meet the dynamic needs of modern workplaces. The aim of this study is to design a novel intelligent training system that can accurately predict future vocational skill needs and provide personalized training plans based on this. To achieve this goal, the article combines deep learning techniques with skill graph methods. Specifically, by analyzing large-scale occupational datasets, deep learning models can be used to identify key skill transitions and emerging skills. Secondly, constructing a skill graph reveals the correlations and evolutionary paths between different skills; Finally, based on the individual's learning process and career goals, a customized learning path and career development recommendations are provided through an intelligent recommendation system. In terms of career development speed, the e-commerce industry is leading at an average rate of 16 months per promotion, indicating that the system may help e-commerce industry practitioners to achieve rapid career advancement. Research has shown that this intelligent training system can significantly improve the pertinence and efficiency of training, and has important practical value in promoting employment, adapting to changes in industry demand, and personal career development.