Optimizing content creation and distribution in new media operations using deep learning

This paper proposes a new media content creation and distribution optimization model DL-CCD based on deep learning, which realizes the coordinated optimization of content generation and personalized recommendation through multimodal feature fusion and multi-task learning. The model significantly improves semantic consistency and user appeal in content generation tasks, and the BLEU-4, ROUGE-L and METEOR indicators are improved by 20.0%, 17.3% and 25.0% respectively, and the user satisfaction score is improved by 19.4%; in the recommendation distribution task, the Precision@5, Recall@5 and NDCG@5 indicators are improved by 20.0%, 22.5% and 17.8% respectively, and the AUC reaches 0.86, showing superior performance in sparse data environments. The experimental results verify the high efficiency of the model in new media operations and provide technical support for the intelligent content creation and distribution.