This article investigates the application of Artificial Intelligence (AI) in creating and producing digital entertainment and media content by designing an animation content generation algorithm and conducting a simulation study on it. Specifically, Generative Adversarial Network (GAN) is applied to the content creation and generation of digital entertainment and media. GAN consists of two parts: the generator and the discriminator. The task of the generator is to generate content that looks real, while the task of the discriminator is to distinguish the generated content from the real content. In the training process, the two networks will confront each other. The generator tries to generate better content to cheat the discriminator, while the discriminator tries to distinguish the real content from the generated content. Experiments show that compared to other algorithms, the accuracy of this algorithm has certain advantages under the same conditions, which can reach more than 94.89% accuracy with a small error. The animation content generation algorithm designed in this article is superior in error and accuracy. The algorithm can generate high-quality and accurate animation content by optimizing the training process and the learned feature representation. It has a broad application prospect in the fields of digital entertainment and media, which can inject new vitality into the development and progress of this field.