Educational Quality Evaluation Model Based on Deep Learning Theory

In order to solve the problem that the expression ability and generalization ability of shallow learning networks to complex functions are limited, and to improve the accuracy of college education quality evaluation, a college education quality evaluation method based on a deep learning network is proposed. Starting from the three aspects of the educational environment, educational quality, and student development, this paper constructs an educational quality evaluation index system including three primary indicators and nine secondary indicators. Take the secondary index in the evaluation index system as the input of the deep learning network, optimize the weights of each layer of the deep learning network by using the unsupervised pre-training model, determine the conditional probability distribution and joint probability distribution of each layer in the restricted Boltzmann machine (RBM) based on the bottom-up unsupervised learning process, and the output layer optimizes the parameters of each layer according to the input differential mean opinion score (DMOS) value and constructs the regression model between the abstract primary index and DMOS value, The objective evaluation results of education quality are obtained according to the prediction of a regression model. The test results show that the linear correlation coefficient and grade correlation coefficient between the evaluation results of this method and the subjective evaluation results are closer to 1.