Author(s)

Qingying Lin

Corresponding Author

Qingying Lin

Abstract

This article studies the employment quality of university graduates. Under the complicated and changeable background of the current employment situation, the research aims to build a real-time and dynamic employment quality monitoring system. By integrating the academic data on campus, the data on off-campus recruitment platform and the macro-industry data, drawing lessons from the analysis framework of large-scale Internet job hunting behavior, this article uses NLP (natural language processing) technology to analyze the job skills demand, and constructs a dynamic monitoring index and early warning model. Moreover, with the advantage of obtaining unique multi-dimensional data on campus, the feedback and intervention mechanism is put forward from the perspective of enrollment-training-employment linkage governance. The system can effectively monitor the change of employment quality, and the early warning model can detect potential risks in advance. This study provides a scientific basis for universities to optimize talent training strategies, adjust enrollment plans and enhance students' employment competitiveness, which is of great significance to improve the employment quality of university graduates.

Keywords

Multisource big data; Employment quality; Dynamic monitoring; Early warning model; Linkage governance