Biotechnology-Enhanced Musical Audience Satisfaction and Evaluation Model Construction

Yan Hu
Management School, University of Stirling, Stirling FK9 4LA, UK
Jing Wang
School of Humanities and Social Science, University of Science and Technology Beijing, Beijing 100083, China.

Abstract:

As a unique art form, musical has vibrant expressive power, and a single element cannot define its charm. Musical integrates multiple artistic elements, such as music, dance, drama and visual effects, and has strong commercial and cultural characteristics. It transcends the boundaries of language, nationality and culture, and is deeply loved by audiences around the world with its audio-visual fusion. With the development of biotechnology and artificial intelligence, deep learning algorithms have achieved remarkable results in many fields, and the evaluation of audience satisfaction in music has therefore ushered in a new research opportunity. This paper proposes a deep learning algorithm model based on the concept of biotechnology to study and evaluate the satisfaction of musical audiences. Drawing on the understanding of complex biological systems in biotechnology, this paper constructs a multi-level and complex audience satisfaction evaluation system, which can more comprehensively capture and analyze the audience's multi-dimensional emotions and experiences while watching musicals. The research finds that the deep learning algorithm adopted has significant advantages in processing big data and non-linear relationships, and its accuracy is higher than that of traditional evaluation methods. This paper also explores the commonalities between deep learning and biotechnology, emphasizing the role of technology in promoting the development of art forms