In recent years, the application of Artificial Intelligence Generated Content (AIGC) technologies in artistic creation has rapidly expanded, producing a wealth of new visual, musical, and literary works, while transforming traditional modes of artistic production. However, challenges remain in the digital representation and generation of Chinese traditional art, particularly landscape painting, where technical and cultural integration is often difficult. Existing AIGC tools typically struggle to accurately replicate the subtle artistic essence of traditional Chinese paintings. This study focuses on the dynamic generation of the landscape painting style of He Xiangning, a prominent revolutionary and artist, known for her distinctive style that merges traditional aesthetics with personal expression. By leveraging AIGC technologies, this research not only aims to digitally preserve and transmit traditional art but also to offer a novel means for the public to reappreciate its artistic value. Additionally, with the progress of human-computer interaction technology, multi-modal interaction methods (such as gesture, voice, and touch) are becoming increasingly important in artistic creation, allowing for “co-creation” between humans and machines. This study explores the synergistic application of AIGC technologies and multi-modal interaction in the transmission of traditional art, while also evaluating the system’s user experience through an extended Technology Acceptance Model (TAM). Key contributions of this study include: The development of a dynamic generation system that integrates AIGC and multi-modal interaction technologies, achieving an innovative fusion of traditional art and modern technology. Investigation of real-time gesture data control to enhance the user interaction experience and create a new mode for digital art creation. Evaluation of the system's perceived ease of use, perceived usefulness, and perceived enjoyment based on the extended TAM model.