A Comparative Study on the Forecast Models of the Enrollment Proportion of General Education and Vocational Education

Abstract

Predictive research on the enrollment proportion of general education and vocational education is crucial to optimizing the regional talent structure and industrial structure adjustment. The reasonable enrollment proportion of general education and vocational education also plays an important role in the adjustment of the overall employment structure and the development of the regional economy. Therefore, it is imminent to seek a more accurate and reliable prediction model of the enrollment proportion of general education and vocational education. Based on the grey prediction model, exponential smoothing model, ARIMA model and BP neural network, and with the data of the enrollment proportion of all regions in China from 2010 to 2018 as the data sample, the enrollment proportion of each region in 2019 is predicted. By comparing the predicted values with the real values, it is found that the exponential smoothing model has the best accuracy and stability for the enrollment proportion of general education and vocational education forecast. Exponential smoothing model is used to predict the number of high school enrollment and vocational education enrollment, which is of great significance to ensure the reasonable structure of human resources in various regions and promote the coordinated development of the education system.