The article "User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology" explores the integration of artificial intelligence (AI) into user interface (UI) design to enhance user experience and promote innovation. The authors, Xuanyi Li, Haotian Zheng, Jianlong Chen, Yanqi Zong, and Liqiang Yu, highlight the importance of AI in addressing challenges such as inconsistent user experiences, lack of personalization, and accessibility issues. They emphasize the role of experience metrics in quantifying user interactions and optimizing UI design through data-driven approaches. The introduction traces the evolution of UI paradigms from batch processing to the current focus on AI-driven interactions. AI's ability to predict user behavior and preferences is discussed, along with its applications in personalized recommendations, natural language processing, and dynamic conversation flows. The article also compares command-based interfaces (CUI) with traditional graphical user interfaces (GUIs), highlighting the advantages of CUI in terms of natural language interaction, dynamic conversation flow, intelligent feedback, and data-driven optimization. The authors provide several case studies to illustrate the practical applications of AI in UI design. These include personalized recommendation systems on platforms like Netflix and Amazon, AI-driven augmented reality (AR) and virtual reality (VR) experiences, and automated design processes using AI tools such as Adobe Sensei. These examples demonstrate how AI can enhance user engagement, improve efficiency, and create more immersive and personalized interactions. The conclusion emphasizes the ongoing trend of AI in UI design, its potential to drive innovation, and the need to address challenges and explore new solutions. The authors express confidence that future UI design will be more intelligent and personalized, better meeting user needs and expectations. The article concludes with an acknowledgement of the contributions of other researchers, particularly Yanlin Zhou, whose work on AI-enhanced multi-omics integration provided valuable insights and methodologies for predictive modeling of disease susceptibility.The article "User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology" explores the integration of artificial intelligence (AI) into user interface (UI) design to enhance user experience and promote innovation. The authors, Xuanyi Li, Haotian Zheng, Jianlong Chen, Yanqi Zong, and Liqiang Yu, highlight the importance of AI in addressing challenges such as inconsistent user experiences, lack of personalization, and accessibility issues. They emphasize the role of experience metrics in quantifying user interactions and optimizing UI design through data-driven approaches. The introduction traces the evolution of UI paradigms from batch processing to the current focus on AI-driven interactions. AI's ability to predict user behavior and preferences is discussed, along with its applications in personalized recommendations, natural language processing, and dynamic conversation flows. The article also compares command-based interfaces (CUI) with traditional graphical user interfaces (GUIs), highlighting the advantages of CUI in terms of natural language interaction, dynamic conversation flow, intelligent feedback, and data-driven optimization. The authors provide several case studies to illustrate the practical applications of AI in UI design. These include personalized recommendation systems on platforms like Netflix and Amazon, AI-driven augmented reality (AR) and virtual reality (VR) experiences, and automated design processes using AI tools such as Adobe Sensei. These examples demonstrate how AI can enhance user engagement, improve efficiency, and create more immersive and personalized interactions. The conclusion emphasizes the ongoing trend of AI in UI design, its potential to drive innovation, and the need to address challenges and explore new solutions. The authors express confidence that future UI design will be more intelligent and personalized, better meeting user needs and expectations. The article concludes with an acknowledgement of the contributions of other researchers, particularly Yanlin Zhou, whose work on AI-enhanced multi-omics integration provided valuable insights and methodologies for predictive modeling of disease susceptibility.