Development of Online Teaching Platform for Art Design Specialty Based on Deep Learning Algorithm

At present, with the rapid growth of network and informatization, art education and network information technology have been combined, and informatization education reform and development plans have been introduced one after another, and the pace of education modernization has been accelerated. In this article, DL algorithm is used to model and analyze user preferences, and the obtained user model vectors are clustered. In order to facilitate the recommendation of online instructional resources tailored for art design majors through the utilization of big data, this aims to offer substantial backing for the development of an online teaching platform dedicated to art design education. The results show that the deep learning (DL) algorithm in this article is significantly higher than the traditional collaborative filtering (CF) algorithm in recommending online instructional resources for art design. Therefore, it is of positive significance to apply the personalized recommendation model of instructional resources for art design specialty to the construction of online teaching platform for art design specialty. The creation and utilization of an online teaching platform present a novel trajectory for evolving the conventional teaching methodology in the field of art design. This, in turn, establishes a robust groundwork for enhancing the overall quality of online education within the art design specialty.