In order to provide more effective employment guidance work in colleges and universities, and train students in a more targeted manner, this study collects the relevant information of graduates and their employment situations, constructs a classification prediction modeling algorithm based on HMIGW feature selection and XGBoost, and applies it in graduates' employment forecasting. In consideration of the mixed discrete-continuous characteristics of the student information data, the study proposes an HMIGW feature selection algorithm suitable for employment prediction. This method firstly correlates the characteristics of student data, then adopts forward-increasing backward recursive deletion strategy to conduct feature selection. Finally, the XGBoost prediction model is used for training and result prediction based on the selected optimal feature subset data. By comparing the results of different algorithms, the prediction method adopted in this study has a better performance in evaluation indexes such as accuracy and time, and has a positive effect on employment guidance of graduates.