With the rapid development of Artificial Intelligence technology, programming education is transforming from traditional grammar instruction to cultivating computational thinking and practical problem-solving abilities. Python, as a widely adopted programming language, urgently needs innovative teaching methods. While project-based learning aligns with the inherent needs of programming education, it still faces challenges in practice, such as limited project selection, high student cognitive load, and simplistic evaluation methods. The emergence of Generative Artificial Intelligence offers a new path to address these issues. Take DeepSeek as an example, this paper systematically constructs a Python project-based teaching model empowered by Generative Artificial Intelligence. Focusing on three stages—project introduction, activity exploration, and results presentation—it elaborates on the collaborative mechanism between teachers, students, and GenAI in six stages: project selection, planning, activity exploration, project creation, results sharing, and activity evaluation. It provides a systematic theoretical framework and instructional design reference for Python project-based teaching supported by generative artificial intelligence, laying the foundation for subsequent empirical research and practical exploration.