China’s higher education development evaluation based on GA-BP neural network

Abstract

The development of higher education supplies a large number of high-level talents to the society, which is the key to building a harmonious society. At present, the development of regional higher education is extremely uneven, and it is the top priority of education development that it is urgent to clarify the situation of regional higher education. This article constructs a comprehensive evaluation index system of higher education development from a total of 19 indicators from five dimensions of talent training, teacher strength, scientific research output, infrastructure and social services, and then uses entropy and genetic algorithm-projection pursuit model to calculate the weight. GA-BP and BP neural network models are used for comprehensive evaluation. It is found that: (1) The most important factors affecting the development of higher education are technology transfer income and the application of R&D achievements in colleges and universities; (2) Compared with BP neural network, GA optimizes BP neural network in terms of effectiveness, convergence speed, and accuracy. (3) Generally speaking, during the research period, the development of China’s higher education has gradually improved, with an average annual growth rate of 3.5%. In terms of sub-regions, the provinces with excellent higher education development levels have increased from 0 in 2008. The number has increased to 5 in 2019, and the development of higher education among provinces is extremely uneven, and the differences between provinces are gradually increasing.