Prediction of the Development Scale of Vocational Education Using Markov Algorithm and Countermeasures

Abstract

HVE (Higher Vocal Education) in China is developing rapidly. With the expansion of HVE enrollment, the scale has also expanded rapidly. However, with the great development of HVE, HVE also faces many problems. In this paper, the M_BPNN (Markov neural network-BP) model is constructed for the prediction of the development scale of vocational education. Using BPNN’s powerful nonlinear mapping ability and error correction thought, the data information of the future development scale of vocational education is predicted. The results show that the prediction accuracy of the M_BPNN model is the best, and MSE (mean squared error) and MRE (mean relative error) are 10.184 and 5.017, respectively, which are lower than the other two prediction models. It shows that the prediction effect of the M_BPNN model is better than that of the pure Markov model. The forecast results show that the population of school age in H province will decrease from 3.36 million in 2020 at an average annual rate of nearly 700,000 to the lowest value of 2.06 million in 2022. After that, the population of school age will increase steadily. The result shows that there is a relative shortage of regional students, and the enrollment scale is developing well, but it is still not optimistic. It is necessary to coordinate the cross-regional development.