Forecast of Advanced Human Capital Gap Based on PSO-BP Neural Network and Coordination Pathway: Example of Beijing–Tianjin–Hebei Region

Abstract

The upgrading of human capital caused by education is significant to regional development. Reasonable predictions of the degree of advanced human capital in different regions are effective for formulating reasonable talent policies and accelerating regional coordinated development. The BP neural network is a widely used prediction technology. PSO-BP neural network has good global search ability, which can accelerate the convergence speed of traditional BP neural network, which is suitable for forecasting larger data. The study takes the provincial data of China from 2005 to 2019 as an example, using PSO-BP neural network algorithm to predict the advanced level of human capital through the influencing factors filtered by OLS regression. The results show that: (1) Innovation ability and urbanization can play a decisive role in advanced human capital filtered by OLS regression; (2) The results of predicting the development trend of advanced human capital in the Beijing–Tianjin–Hebei region in 2020–2025 through the PSO-BP neural network have showed that there is still a large gap between the senior human capital stock in Hebei-Beijing-Tianjin in terms of total and per capita in 2020–2025 compared with other regions in east of China; (3) Giving full attention to elaborate the positive role of economic quality and quantity development are suitable for narrowing the difference of advanced human capital in this region. Through the method of OLS-BP-neural network, this study explores the gap and influencing factors of the Beijing–Tianjin–Hebei region, excavates the reasons for the huge gradient difference in the development of this region, and extends the machine learning prediction method to the analysis of the advanced level of human capital and the research of narrowing the regional development gap.