Evaluation on algorithms and models for multi-modal information fusion and evaluation in new media art and film and television cultural creation

Abstract

This paper promoted the development of new media art and film and television culture creation through multi-modal information fusion and analysis, and discussed the existing problems of new media art and film and television culture creation at present, including piracy, management problems and lack of innovation ability. The network structure of RNN neural network can cycle information among neurons, retain the memory of previous user information in the progressive learning sequence, analyze user behavior data through previous memory, accurately recommend users, and provide artists with a basis for user preferences. The viewing experience scores for works 1 to 5 created using traditional creative methods were 6.23, 6.02, 6.56, 6.64, and 6.88, respectively. The viewing experience scores for works 1 to 5 created through multi-modal information fusion and analysis were 9.41, 9.08, 9.11, 9.61, and 8.44, respectively. Movies created through multi-modal information fusion and analysis had higher viewing experience ratings. The results of this article emphasize that multi-modal information fusion and analysis can overcome the limitations of traditional single creative methods, provide rich and diverse expressions, and enable creators to more flexibly respond to complex creative needs, thereby achieving better creative effects.