Purpose Faced with the structural employment contradiction (background) caused by the growth of more than 15% in the gross enrollment rate of higher education and the acceleration of technological iteration in the process of globalization, this paper aims to develop an intelligent system that can accurately predict the employment path of fresh graduates and solve the defects of traditional methods in data imbalance, dynamic adaptability and privacy protection (purpose). Design/methodology/approach By constructing a hypergraph network based on the relationship between dormitories and courses, this paper innovatively integrates pseudo-label strategy (reinforcement learning to screen real nodes), triple data enhancement (feature masking/hyper-edge modification/graph diffusion) and heterogeneous encoder contrast learning method (L = 2 local layer cooperates with L = 4 global layer). Findings In addition, through experimental verification, this paper finds that the model achieves 89.1% accuracy rate and 86.5% minority recall rate in four types of employment status prediction, and the noise robustness is improved to-8.2%. At the same time, the actual deployment has increased the accuracy of entrance prediction by 19% and the enterprise matching degree by 17%, and multi-modal fusion (cross-modal comparative learning) has further pushed the accuracy to 95.1% (result). Therefore, despite the limitations of the model such as domain dependence (the accuracy rate of art migration is 71.3%), a 30% increase in computing power costs, and unstructured data privacy risks. Originality/value The model still provides a data-driven solution for solving global “skill misalignment”.
Li et al. (Sat,) studied this question.