Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in-depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.