{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design","author":[{"@type":"Person","name":"Hua Tian"}],"datePublished":"2022","isPartOf":{"@type":"Periodical","name":"Mobile Information Systems"},"identifier":"https://doi.org/https://doi.org/10.1155/2022/6946616","description":"With the development of information technology, digital media art came into being. Digital media art interface design, as a window for direct communication with users, has a direct impact on the user’s experience, so it has attracted more and more people’s attention. In the context of the continuous development of artificial intelligence, artificial intelligence algorithms have penetrated into all fields of social development. This article is based on the integration of graphic elements with artificial intelligence algorithms to build a digital media art interface design system. According to the analysis of system requirements, combined with the algorithm of artificial intelligence graphic elements, the system hierarchy is constructed from the user model, window model, and display model; software development and driver development are realized through the media library, and the window and common controls are realized. On this basis, the creation and display of windows and common controls are realized, and the design of the core functions of the system is completed to ensure the effect of system functions. The final conclusion shows that in actual design applications, the algorithm in this paper has a very high probability of finding the optimal solution, about 90%, when the number of iterations is small. Adjust parameters α and β, when \n \n α\n =\n 0.6\n \n and \n \n β\n =\n 0.4\n \n , are the best parameters; the image reaches the highest PSNR value, and the image restoration quality is the best; after system testing, the running time of this system is shortened by an average of 1-2 seconds, and the target detection task accuracy rate is 100%; the system is evaluated by questionnaires. The overall satisfaction of the design interface remains at about 80%, which is a great improvement compared to the previous one. Finally, it satisfies the requirements of the digital media art interface design better, and it is worthy of further exploration.","url":"https://innscience.ca/en/community/literature/c4bd33a9-8d38-48a9-8d70-4ca2f3db023e"}