基于多特征融合的GRU-LSTM大学生就业动态预测  

College Students Employment Dynamic Prediction of Multi-feature Fusion Based on GRU-LSTM

作　　者：张剑[1] 张烨[1] ZHANG Jian;ZHANG Ye(College of Communication and Information Engineering,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]西安科技大学通信与信息工程学院,西安710054

出　　处：《计算机科学》2023年第S01期906-911,共6页Computer Science

基　　金：国家自然科学基金青年科学基金(61705178)。

摘　　要：针对高校就业预测系统大多采用单一传统特征建模而导致出现就业预测效果不佳、就业精准服务不强等问题,提出一种融合多特征因素的GRU-LSTM组合就业预测方法。首先,在传统预测模型特征的选择上加入了学生行为特征,并构建了多信息融合的特征向量;然后,结合不同影响因素对高校就业的贡献不同,提出了一种基于皮尔逊相关系数的多信息融合的就业预测最优特征提取方法,优化了特征子集;最后,综合考虑预测精度和预测时间两个方面的因素,提出了一种基于门控循环单元(GRU)与长短期记忆网络(LSTM)的组合预测模型GRU-LSTM,结合LSTM预测精度高与GRU预测时间短的优点对就业数据进行高效精准预测。实验结果表明,该方法与传统方法相比,就业预测的精确率提高了4.2%,对提高大学生就业提供了可靠的数据支撑。At present,the employment prediction system of colleges and universities mostly adopts single traditional feature mo-delin g,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.

关 键 词：深度学习 LSTM 就业预测 数据挖掘 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]