计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220500056-6.doi: 10.11896/jsjkx.220500056
张剑, 张烨
ZHANG Jian, ZHANG Ye
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。
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