Empirical study on a prediction model for the unemployment rate of college graduates based on principal component analysis and BP neural network

doi:10.3969/j.issn.1000-1565.2018.01.015

Abstract

Abstract: A prediction model for the unemployment rate of college graduates was developed based on principal component analysis and BP neural network, followed by analyzing the unemployment data of college graduates in Hebei province in the years 1995—2016.The results show that the model can effectively reflect the variation tendency of the unemployment rate of college graduates in Hebei province and the prediction accuracy of the model is higher than that of the model only based on BP neural network.

Key words: principal component analysis, BP neural network, college graduate, unemployment rate prediction

CLC Number:

TP183

LIU Zhao. Empirical study on a prediction model for the unemployment rate of college graduates based on principal component analysis and BP neural network[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(1): 99-104.

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