This paper establishes a “one-stop” student community party building system for universities based on field theory. From the perspective of technological empowerment in field theory, this paper utilizes big data technology to establish a party building digital profile and employment support system architecture, pushing personalized job opportunities to guide students in their employment. The paper selects employment data of college students from a certain university from 2016 to 2025 as the research sample to explore the reliability of the system and algorithm proposed in this paper. Research findings indicate that the employment support system can visually present specific student employment status through charts. Functional testing demonstrates that the system effectively enhances the efficiency of employment management operations. Comparisons between the employment recommendation results of this algorithm and traditional recommendation algorithms reveal higher predictive accuracy.
