Student employment forecasting model based on random forest and multi-features fusion

Zhenguo  Xing; Xiao  Wu; Jiangjiang  Li

doi:10.6180/jase.202602_29(2).0008

Student employment forecasting model based on random forest and multi-features fusion

Zhenguo Xing¹, Xiao Wu², and Jiangjiang Li¹This email address is being protected from spambots. You need JavaScript enabled to view it.

¹School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450064 China

²School of Finance and Economics, Zhengzhou University of Science and Technology, Zhengzhou, 450064 China

Received: March 14, 2025
Accepted: May 8, 2025
Publication Date: May 30, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202602_29(2).0008

Most of the traditional university employment forecasting systems adopt a single traditional feature modeling, which leads to poor employment forecasting effect and weak employment accurate service. Therefore, this paper proposes a novel student employment forecasting model based on random forest and multi-features fusion. Firstly, the student data is preprocessed to remove irrelevant attributes to achieve data consistency. Secondly, in order to improve the accuracy of the prediction model, a feature selection model combining principal component analysis and random forest algorithm is used to select the optimal subset from the original features. Finally, considering the prediction accuracy and prediction time, a combined prediction model GRU-LSTM based on gated recurrent unit (GRU) and long short-term memory network (LSTM) is proposed. The employment data is forecast efficiently and accurately. The experimental results show that compared with the advanced methods, the accuracy of employment prediction is increased by 4.2%, which provides reliable data support for improving the employment of college students.

Keywords: student employment forecasting; random forest; multi-features fusion; gated recurrent unit; long short-term memory network

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