AI-Driven Audience Development and Cultural Identity Construction: Optimizing Audience Attraction Strategies for Small Theatres and Live Venues through Data Analysis and Behavior Prediction

Under the impact of the digital wave, small and medium-sized theaters and live performance venues are facing the dual challenges of reducing passenger flow and intensifying competition from commercial entertainment platforms. This study explores the synergistic effect of artificial intelligence technology in optimizing audience expansion strategies and strengthening cultural identity, and builds a multidimensional analysis model through K-means clustering, affective semantic analysis, and random forest algorithms. Based on operational data from 37 venues in Beijing, Kuala Lumpur, and Melbourne, the study accurately identified five types of audience groups and their behavioral characteristics, and established a correlation model between performance planning and passenger flow forecasting. Empirical data shows that personalized recommendation strategies can increase the attendance rate of target groups by 14%, and optimized programming combined with audience feedback can increase the satisfaction index by 23 percentage points. The research proves that artificial intelligence can not only achieve precision marketing, but also strengthen the identification of the value of cultural space through emotional resonance analysis, and provide decision support for regional cultural venues to maintain their uniqueness in digital competition.