Data-Driven Employment Support Strategies via Student Group Portrait Analysis

In the context of significantly increased employment pressure for college graduates in the post-pandemic era and the development of the information age led by big data, it is urgent to innovate employment guidance methods and optimize the employment service system. This study focuses on the application of big data technology in employment guidance in colleges and universities. Based on a sample survey of over 1,000 undergraduates, it conducts multi-dimensional feature extraction from 15 indicators such as academic performance, participation in competitions, and positions held in student organizations, and builds a comprehensive student group portrait. Subsequently, a machine learning algorithm is used to establish a prediction model for students’ employment tendencies. The experimental results show that the prediction accuracy of this model reaches 79.62%, which is highly consistent with the actual results. This study aims to provide theoretical and technical support for improving the precision of employment services in higher education and promoting high-quality employment for students. The research results indicate that data-driven prediction models can effectively identify students’ employment tendencies, and refined support strategies have significant practical value in alleviating the employment pressure faced by college graduates.