Design of Adaptive Iterative Reconstruction Method for Music Curriculum Integration and Reconstruction

Abstract

Today, when music education generally attaches great importance to the construction of the subject curriculum, it is of practical significance to increase the research and implementation of curriculum integration and reconstruction. Curriculum integration and reconstruction not only provide a scientific concept guide for the formation of a good educational joint force in music education but also play an important role in the educational reform that focuses on human harmony and subsequent development. For the integration and reconstruction of music courses, this paper evaluates the teaching quality after integration and reconstruction through the neural network and then iterates the method of integration and reconstruction, so as to achieve the optimal effect. This paper introduces the development status of curriculum integration and reconstruction at home and abroad and provides a theoretical basis for the construction of the corresponding index system in the following sections. The related principles of Convolutional Neural Network (CNN) and Generalized Regression Neural Network (GRNN) are introduced, and an education quality evaluation system for music courses is constructed. We constructed a custom data set and introduced the design ideas and the specific parameter settings of the CNN-GRNN model. In addition, the CNN-GRNN model and the CNN-BP (CNN backpropagation) model are compared and analyzed in terms of the quality assessment accuracy of music courses. The results show that the CNN-GRNN model proposed in this paper outperforms the other methods. The mean squared error (MSE) of the model using one-dimensional convolution is lower than that of the CNN-GRNN model using two-dimensional convolution. As compared to CNN-BP model, the CNN-GRNN model outputs results that are closer to the expert evaluation results and the error is small.