Aiming at the practical dilemmas of "inaccurate learning, inefficient training and outdated teaching" existing in practical training of medical aesthetics technology major in higher vocational colleges, this paper constructs a "three-stage · double closedloop" AI-empowered practical teaching model supported by constructivism, experiential learning theory and TPACK framework. Guided by the concepts of data-driven, human-machine collaboration and personalized learning, this model establishes a threestage implementation chain of "pre-class precise diagnosis-in-class intelligent assistance-after-class personalized expansion", and designs dual closed-loop mechanisms including "teaching feedback" and "learning-practice reflection". Its feasibility is evaluated from three dimensions: theoretical consistency, policy compatibility and technical maturity, and four types of supporting conditions including teachers, platform, resources and evaluation are clarified. This path is logically self-consistent in theory and has implementation foundation in policy and technology, which provides an operable and popularizable scheme for the digital upgrading of practical training teaching of Medical Aesthetics Technology major in higher vocational colleges.