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Abstract In view of the characteristics of high-dimensional, unbalanced and multi-category employment data, in order to further im-
prove the accuracy of decision tree method in the employment prediction of college students, an employment prediction model based
on LightGBM is proposed. First the improved ADASYN sampling algorithm is used to increase the minority class in the data sam-
ple, and then the employment data after balance is used for training LightGBM algorithm, and Bayesian model is used for parameter
optimization to get the final employment prediction. Finally the prediction model is analyzed to measure the influence of each fea-
ture on employment. The validity of the proposed method is verified through the data set of unbalanced employment data of college
graduates, and compared with various unbalanced classification methods. It is proved that the proposed model has better prediction
performance.
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