Modern Art Design System Based on the Deep Learning Algorithm

Abstract

Depending on the rapid growth of technology and computer science and globalization, increasing universities and various training programs are expected to achieve increasing lifelong education currently. Based on the increase of visual saturation, one of these programs is undoubtedly visual arts creation. Nowadays, there is a need to reconsider the content of art education and arts programs in the light of modern art productions, changing aesthetic judgments, and social changes.One of the most challenging tasks an art teacher faces in today‘s classroom is explaining the meaning behind the art that is sometimes deemed meaningless or shocking. Hence, in this paper, Deep Learning Algorithm (DLA) has been proposed for modern art education.Profound learning outcomes in the art world shifted people’s traditional views; more and more people consider that when AI eventually eliminates artists, it is not a long way. To examine the algorithm’s performance in practise, experimental assessment exposes it to learning tasks. Such algorithmic features are frequently evaluated independently in terms of performance while learning a model, i.e. at training time, and performance while applying a learnt model, i.e. at test time. This article discusses the implementation of deep learning in drawing, music, literature, and describes the classical models and algorithms in those fields of art. One of the most significant aspects of utilising a deep learning technique is its capacity to perform feature engineering on its own. In this technique, an algorithm searches the data for characteristics that correlate and then combines together to facilitate quicker learning without being expressly directed to provide it. The proposed method supports a comprehensive approach to teaching and learning in the arts and outlines standards and skills for the art teacher in the content of art, knowledge of the students, instruction, curriculum development, and evaluation of student learning outcomes, program, and teacher effectiveness.