Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network

Abstract

In order to improve the reliability of higher education quality evaluation, this paper improves the RBF neural network algorithm based on the characteristics of higher education teaching data. Using RBF neural network technology, the predictive model established after learning from samples of actual teaching data can be used to evaluate teaching quality. Moreover, this paper combines the improved RBF neural network to construct a higher education teaching quality evaluation model. The model built in this article needs the support of a support vector machine (SVM) to build a sentiment analysis module and a teaching quality evaluation module. After the model is constructed, the performance of the model built in this article is evaluated, and the performance of the model is displayed by combing statistics. The teaching quality assessment of the higher education model is between 80% and 85%.The test results show that the higher education teaching quality appraise model depended on the improved RBF neural network put forward in this paper has certain effects.