A Neural Network and Genetic Algorithm-Based Model for Evaluating and Enhancing Quality-Oriented Teaching Systems

Quality-oriented education is an educational system that focuses on enhancing the quality of education, emphasizing the cultivation of moral integrity and abilities. In the new era, facing new challenges and opportunities, quality-oriented education for the youth has become a top priority. This paper focuses on four aspects: what quality-oriented education is, the differences in its implementation between domestic and international settings, the significance of quality-oriented education as I see it, and how to realize personal value through quality-oriented education. To scientifically evaluate the quality of teaching, this thesis adopts the Back-Propagation (BP) neural network model to assess and analyze the quality of university quality-oriented education. The results show that by accelerating the convergence efficiency using a combination of the BP neural network and Genetic Algorithm (GA) model, the accuracy of its evaluation and prediction is obviously improved. Compared with convolutional neural network and deep neural network, the experimental data indicate that the Mean Squared Error (MSE) of predictions using the BP-GA approach is at its minimum, with the lowest value being 8.5E–05 and the highest 1.914E–04.