Modeling Graduates’ High Quality Employment Based on Support Vector Machine

Abstract

In this study, the training data of graduate students of Xi'an University of Posts and Telecommunications for the past three years was used to construct a predictive model for graduate students' high-quality employment based on support vector machine through 13 feature attributes that may affect the high-quality employment of graduate students.Six important characteristics of high quality employment were studied, including gender, student origin, postgraduate score, innovation fund, book reading and graduation thesis score. After many experiments, the accuracy of the model was 87.41%.According to the calculation results of the model, it is concluded that the graduate students should make corresponding improvements in the postgraduate training sessions, encourage graduate students to engage in cutting-edge and groundbreaking research work; continuously expand the knowledge of students; strengthen the guidance of instructors, improve graduate students' ability to analyze and solve problems.