Research on Digital Creative Content Generation System Based on Artificial Intelligence

A new algorithm framework is proposed, which combines the self-attention mechanism and the transformer model, to enhance the efficiency and quality of the creation of digital creative content. The system realizes the semantic understanding and generation tasks of multimodal data by designing a multi-layer Transformer architecture, and fully exploits the context modeling ability of the self-attention mechanism. To verify the performance of the algorithm, this paper simulates the model in image generation, text generation and multimodal content generation tasks. The test results show that the system has achieved significant improvements in the coherence, diversity and innovation of the generated content. Experiments on a public dataset show that the BLEU score of the generated text content is 12.3% higher than that of the traditional method, the FID index of image generation is reduced by 15.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, and the semantic consistency score of multimodal generation reaches 92.4%. These results prove the superiority and practicality of the proposed algorithm. This research not only provides a new way to generate digital creative content, but also provides a theoretical basis for the further integration of AI and creative industry.