In the 21st century, the research and development of all kinds of intelligent equipment have become an increasingly important part of people’s daily life. Various emerging technologies are developed based on hardware equipment, such as augmented reality (AR) and virtual reality (VR), which fully utilize computer vision technology to accomplish various purposes. Nowadays, it is difficult to carry out opera popular science education in China. Because opera teaching is boring and some traditional opera knowledge is obscure and difficult to understand, it cannot arouse students’ interest in learning. Therefore, this article uses VR technology to stimulate students’ learning initiative and complete drama science popularization education with rich and colorful teaching means. It first briefly introduces the concept and characteristics of AR technology. After that, it establishes an AR-enabled drama application as an education model based on the statistical entity recognition algorithm and designs the drama popular science education app. Besides, it completes the drama animation design, character modeling design, drama scene design, logo pattern design, and animation design. Finally, from the two points of students’ mastery of opera and learning opera knowledge, the actual use effect of this application is analyzed. The results show that this application can help students better learn opera: 42% of students can better master opera singing, 23% of students say they can master other types of opera, 18% of students can better learn and master opera movements and posture, and 10% and 7% of students can master a lot of opera knowledge.

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.