The Evaluation and Application of BP Neural Network in the Multiple Quality Assurance Paths of Higher Education

Abstract

China's higher education, with colleges and universities as its main part, provides public services, performs public functions and realizes public interests, so it has public attributes. Since the development of China's higher education, it has been popularized nationwide with a gross enrollment rate of higher education over 50 percent. The quality of higher education is related to the level of talents for the society in the future. Therefore, colleges and universities need to improve the level of teaching and the quality of talent cultivation. In this context, the multi-quality teaching of higher education has become a hot topic of discussion. This paper uses the BP neural network evaluation model to evaluate the teaching quality. The principal component analysis method is used in the research evaluation index, and the main components are extracted from the evaluation index to calculate the evaluation score. Through quantitative research, the school education can be obtained. The quality evaluation results and the test results prove that the selection of evaluation indicators is reasonable and can accurately reflect the quality of school education, thus reflecting the scientificity and feasibility of teaching quality evaluation.