基于大数据集成技术的高校毕业生就业率预测研究  

Research on college graduate employment rate prediction based on big data integration technology

作　　者：张志辉[1] 刘增奇[2] ZHANG Zhihui;LIU Zengqi(Yanshan University,Qinhuangdao 066004,China;Hebei Normal University,Shijiazhuang 050024,China)

机构地区：[1]燕山大学,河北秦皇岛066004 [2]河北师范大学,河北石家庄050024

出　　处：《现代电子技术》2021年第4期79-82,共4页Modern Electronics Technique

基　　金：秦皇岛市科学技术研究与发展计划项目(201901B031)。

摘　　要：为了获得高精度的高校毕业生就业率预测结果,提出基于大数据集成技术的高校毕业生就业率预测方法。首先采集高校毕业生就业率的历史数据,并进行标准化处理,获得高校毕业生就业率预测的学习样本;然后采用大数据集成技术,即BP神经网络和支持向量机,分别对高校毕业生就业率进行建模与预测,并通过合理加权方式得到高校毕业生就业率预测结果;最后与单一BP神经网络和支持向量机进行高校毕业生就业率预测仿真对比实验。结果证明,所提方法的高校毕业生就业率预测精度分别高于BP神经网络和支持向量机10%和5%左右,降低了高校毕业生就业率预测误差,可以应用于实际的高校毕业生就业管理系统中,具有较高的应用价值。A method of college graduates employment rate prediction based on big data integration technology is proposed to obtain high⁃precision prediction results of employment rate of college graduates.The historical data of the employment rate of college graduates are collected and its standardized processing is performed to obtain the learning samples for the prediction of the employment rate of college graduates.The big data integration technology(BP neural network and support vector machine)is used to model and predict the employment rate of college graduates,and the prediction results of the employment rate of college graduates are obtained by means of the reasonable weighting scheme.The simulation experiment of college graduates employment rate prediction was carried out to compare the method proposed in this paper with the single BP neural network and support vector machine.The results show that the prediction accuracy of the employment rate of college graduates of the proposed method is about 10%higher than that of BP neural network and 5%higher than that of support vector machine,which reduces the prediction error of the employment rate of college graduates.It can be applied to the actual employment management system of college graduates and has a higher application value.

关 键 词：高校毕业生 就业率 大数据 就业率建模 实例分析 仿真实验 

分 类 号：TN919-34[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]