Research on Deep Learning of Generative Artificial Intelligence in Cultural Creation and Communication

This study focuses on the application of generative artificial intelligence in the field of cultural creation and communication, and proposes an innovative multimodal fusion generative adversarial network algorithm (MM-GAN). The algorithm effectively integrates multimodal cultural data such as text, image, and audio by constructing a cultural feature fusion module, a dynamic adversarial training mechanism, and a semantic consistency constraint module. The experiment uses public cultural data sets and self-built characteristic cultural corpora, covering scenes such as poetry creation, film and television poster design, and music melody generation. In the poetry generation task, the BLEU-4 score of poetry generated by MM-GAN reached 0.45, which is higher than the 0.32 of traditional GAN and other baseline models. In the film and television poster design experiment, the average perceptual similarity between the generated poster and the target style is 0.88, far exceeding the comparison algorithm. In terms of music melody generation, the average harmonic rationality score of the generated melody is 4.2 (out of 5 points). The results show that MM-GAN is significantly superior to traditional algorithms in terms of cultural creation quality, style fit and semantic coherence, and can effectively assist cultural creation and dissemination, providing a new path for the development of this field.