In order to solve the problems of demand perception lag, training feedback dispersion and recommendation service fragmentation in the “recruit and create” chain of colleges and universities, this paper constructs a full-cycle deep learning employment demand prediction and accurate recommendation system. Based on 84216 job records and student training, internship employment and innovation and entrepreneurship data, this paper establishes a heterogeneous correlation model of job demand, user ability and training resources, and integrates Transformer semantic coding, temporal feature learning and graph relationship modeling to realize the joint prediction of job demand intensity, ability gap and resource compensation path. The system further designs a personalized recommendation and interactive feedback mechanism to form a closed-loop service process of “prediction- matching-intervention-update”. The experimental results show that the Accuracy of the proposed model is 89.3%, the Macro-F1 is 88.1%, and the Top-3 hit rate is 95.6%. In the real application test, the proportion of effective subsequent behaviors triggered after recommendation reaches 72.8%, and the decision time of students’ first effective delivery is reduced from 26.4 minutes to 18.7 minutes. The research provides a deployable computing scheme for the digitalization, collaboration and intelligence of university employment service.
