Big Data Analysis Model for Vocational Education Employment Rate Prediction

Abstract

Vocational education is an important means to promote the development of the national manufacturing industry. To correctly grasp the relationship between vocational education and the labor market, we start with the quality of vocational education and employment rate and study the interconnection between them. To this end, we propose a vocational education employment rate prediction method based on a big data model and build a vocational education quality assessment and employment rate prediction system. We draw on the big data cross-learning model to improve the model hyperparameters by using generalized intersection sets on the joint loss function to compensate for the shortcomings of dense vocational education datasets. We use the GSA algorithm to enhance the local features of different vocational education quality assessment index data series. To scientifically assess the recognition of vocational education, we evaluate the vocational education assessment indexes at the student level and the parent level to verify the reliability of the experiment. The experimental results prove that our method performs best in the prediction accuracy of vocational education quality assessment indicators, and the prediction accuracy rate stays above 91%. In the prediction of vocational education employment rate, the difference between the predicted and actual values of our method is the smallest, and the difference stays within 1%. Compared with other big data models, our method has higher prediction accuracy and better robustness.