Abstract: At present,the employment prediction system of colleges and universities mostly adopts single traditional feature mo-deling,which leads to problems such as poor employment prediction effect and weak employment accurate service.This paper proposes a multi-feature fusion based on GRU-LSTM employment prediction method.Firstly,students’ behavior features are added to the traditional prediction model,and the feature vector of multi-information fusion is constructed.Then,considering the different contribution of different influencing factors to college students employment,an optimal feature extraction method of employment prediction based on Pearson correlation coefficient is proposed to optimize the feature subset.Finally,a combined prediction model of GRU and LSTM is proposed,which combines the advantages of high prediction accuracy of LSTM and short prediction time of GRU to make efficient and accurate prediction of employment data.Experimental results show that compared with the traditional methods,the accuracy of employment prediction by this method increases by 4.2%,providing reliable data support for improving the employment of college students.

Key words: Deep learning, LSTM, Career prediction, Data mining