Author(s)

Qianlin Liu

Corresponding Author

Qianlin Liu

Abstract

With the increasingly fierce competition in the film industry, it is very important to dig deep into audience preferences and formulate effective content generation strategies accordingly. This paper focuses on the research of movie audience preference mining and content generation strategy based on big data analysis. Through the comprehensive application of big data theory and the integration of multi-source data such as social media, online ticketing platform and video website, the preferences of movie audiences are deeply analyzed. With the help of big data analysis, we can have a clear insight into the audience's preferences in subject matter, role, plot, audio-visual effects and so on. For example, movies with different themes show dynamic changes in box office share and search popularity, and the audience has obvious preference for specific character characteristics. Based on these findings, this paper constructs a film content generation strategy theory covering theme and theme selection, role shaping and actor selection, plot structure and rhythm control, and visual and auditory effects. The research aims to help the film industry accurately grasp the needs of the audience, create more competitive works in the market and promote the high-quality development of the film industry.

Keywords

Big Data Analysis; Movie Audience Preference; Content Generation Strategy; Theme Selection; Role-Building