Creation of Drama Art Based on Deep Learning and Evolution Strategy

Abstract

The rapid development of information technology has promoted the growth of deep learning, artificial intelligence, and big data technology. Nowadays, the artistic form of traditional opera creation is accepted and respected by people—more and more people like the art form. However, the artistic creation of traditional opera needs inspiration. The essence of inspiration creation is to reconstruct its objective structure. The deep learning algorithm’s essence is to extract all of the attributes of sample self-learning input data and use them as inspiration for artistic production. First, this paper briefly introduces the deep learning and evolution strategy and uses these algorithms in opera art creation to construct 1 + λ. With the help of this evolutionary algorithm, an optimal solution is obtained through random evolution. The evolution strategy establishes the evolution function matrix. Starting from the situation of students learning opera art, this article examines the process of creating opera art using an in-depth learning and evolution technique. The results show that 96 percent of the students have contact with opera while watching an opera tour. During this, they were not interested in the performance of literary drama in traditional opera. However, it was noticed that they were deeply interested in martial arts, clown performances, and drama stage performances. Finally, the audience group of opera artistic creation is analyzed in the form of opera animation of “A Journey to the West: The Return of the Great Sage.” It signifies that the opera’s leading audience group is aged 25 to 29. However, they account for only 30 percent.