用GCN和LSTM为大学生提供双边就业情况预测模型
1Employment Guidance Division, Luohe Medical College, Luohe, Henan, China.
PeerJ. Computer science
|August 7, 2023
概括
本研究介绍了一种使用图形卷积网络 (GCN) 和长期和短期内存 (LSTM) 预测毕业生就业趋势的大数据模型. 这种新的方法有助于学生的职业规划,并加强了就业能力评估.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 教育技术的教育技术
背景情况:
- 全球化加剧了就业市场的竞争,为学生就业带来了挑战.
- 准确评估毕业生就业能力和就业趋势对于职业指导至关重要.
研究的目的:
- 提出一个新的预后模型,利用大数据技术为毕业生就业.
- 帮助大学生了解就业环境,并提供精确的职业指导.
- 预测就业趋势,评估毕业生的综合素质.
主要方法:
- 开发一个专门的就业平台,用于分析大学生就业数据.
- 实施图形卷积网络 (GCN) 分类模型,以评估毕业生的优缺点.
- 应用长期和短期记忆 (LSTM) 网络来预测大学生就业趋势.
主要成果:
- 提出的方法有效地评估毕业生的综合素质.
- 该系统准确地预测了大学生的就业前景,并具有很高的效率.
- 获得的82.45%和69.89%的F值证明了该模型对双线就业的预测能力.
结论:
- 大数据驱动的模型增强了对毕业生就业能力的评估.
- 准确预测就业趋势有助于学生在竞争激烈的就业市场上进行导航.
- 基于GCN和LSTM的方法为研究生双线就业预测提供了一个新的范式.
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