为了使高校的就业指导工作更具针对性，可以有针对性地培养学生，本文收集了毕业生的相关信息及其各自的就业情况，构建了基于HMIGW特征选择和XGBoost的分类预测建模算法，并将其应用于毕业生就业预测.本文首先考虑到学生信息数据具有离散型和连续型混合的特点，提出一种适应于就业预测的基于互信息和权重的混合（Hybrid feature selection based on Mutual Information and Gain Weight，以下简称HMIGW）特征选择算法，该方法先对学生数据的特征做相关性估值，然后采用前向特征添加后向递归删除策略进行特征选择，最后基于选择后的最优特征子集数据用XGBoost预测模型进行训练与结果预测.通过对比不同算法的结果，本文采用的预测方法在准确率和时间等评价指标上有较好的表现，对于毕业生培养就业指导具有积极作用.
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.