大学生就业类型预测模型构建和对学生就业工作的启示

Construction of a Prediction Model for College Students’ Employment Types and Its Implications for Students Employment Work

摘要: 挖掘大学生就业数据隐含信息有助于实现大学生就业服务的精准化、信息化。本研究采用标准BP神经网络和主成分分析改进的BP神经网络两种方法，探究大学生基本信息与就业类型之间关系，建立大学生就业类型预测模型。大学生就业类型预测模型结果表明：(1) 标准BP神经网络能建立精度良好的大学生就业类型预测模型；(2) 主成分分析改进的BP神经网络模型能提高大学生就业类型预测模型的精确度；(3) 在提取的主成分中，大学生的学业成绩、户籍和住址、专业类别等因素对就业类型预测模型的贡献率最高。研究结果对大学生就业工作有如下启示：(1) 建立全面就业工作数据系统，提取就业预测的关键因素和核心指标；(2) 挖掘就业数据信息价值，建立科学就业信息分析系统；(3) 构建有效的就业预测模型，构建分层分类的精准化就业指导体系。

Abstract: Mining the latent information embedded in college students’ employment data is instrumental in achieving precision and informatization in college students’ employment services. This study utilizes two approaches: the standard BP neural network and the BP neural network enhanced through principal component analysis, to delve into the correlation between college students’ basic information and their employment types, and to construct a predictive model for college students’ employment types. The results of the prediction model for college students’ employment types indicate that: (1) the standard BP neural network is capable of developing a predictive model for college students’ employment types with commendable accuracy; (2) the BP neural network model refined by principal component analysis can further elevate the precision of the predictive model for college students’ employment types; (3) among the principal components extracted, factors including college students’ academic achievements, registered residence, address, and major classification exhibit the highest contribution rates to the predictive model for employment types. The research findings offer the following insights for enhancing college students’ employment services: (1) establish a comprehensive employment work data system to extract key factors and core indicators for employment prediction; (2) mine the value of employment data information and establish a scientific employment information analysis system; (3) construct an effective employment prediction model and build a hierarchical and classified precision employment guidance system.