基于改进随机森林和深度学习的学生就业预测模型

A Student Employment Prediction Model Based on Enhanced Random Forest and Deep Learning

为促进学校和学生更好地应对动态就业市场,提高教育与就业的匹配度,提出一种结合改进随机森林算法(RF)和深度置信网络(DBN)的学生就业预测混合模型。首先,对学生数据进行预处理,去除不相关的属性以实现数据一致性。其次,为了提高预测模型的准确性,采用结合主成分分析(PCA)和RF算法的特征选择模型,从原始特征中选择最优子集。最后,将选定特征作为DBN的输入,学习高级特征表示并完成学生就业预测。实验结果表明,所提混合模型的预测准确度达到了92%以上,相比循环神经网络(RNN)和多层感知机(MLP)模型分别高出16.8%和14%,能够帮助教育机构了解不同专业和课程的就业趋势,改善学生的职业发展路径规划。

In order to better equip educational institutions and students to navigate the dynamic job market and enhance the alignment between education and employment,a blended student employment prediction model,combining the improved Random Forest algorithm(RF)and Deep Belief Network(DBN),is proposed.Firstly,student data undergo preprocessing to eliminate irrelevant attributes,ensuring data consistency.Subsequently,to enhance the accuracy of the prediction model,a feature selection model incorporating Principal Component Analysis(PCA)and the RF algorithm is employed to choose an optimal subset from the original features.The selected features are then utilized as inputs for the DBN,facilitating the learning of advanced feature representations and facilitating student employment prediction.Experimental results demonstrate that the proposed blended model achieves a prediction accuracy exceeding 92%,outperforming Recurrent Neural Network(RNN)and Multilayer Perceptron(MLP)-based models by 16.8%and 14%,respectively.The proposed model proves instrumental in assisting educational institutions in understanding employment trends across various majors and courses,thereby improving students'career development planning.