Ventilator dependence has become a critical challenge in the field of critical care medicine. Among patients with chronic diseases, who often have multiple underlying systemic conditions, the incidence of ventilator dependence, weaning failure, and mortality are significantly higher than those in the general population, along with substantial medical resource consumption. Existing intervention strategies suffer from insufficient systematization, lack of individualization, and weak predicative capability. Based on multidimensional clinical data and AI technology, this paper proposes a full-chain precision intervention framework consisting of four components: risk identification, stratified intervention, dynamic monitoring and follow-up management. By integrating standardized data collection, construction of explainable AI models, phenotype-oriented modular interventions, and multicenter validation, it enables closed-loop management from risk prediction to clinical practice, providing theoretical and practical guidance for improving prognosis of ventilator-dependent patients with chronic diseases and optimizing medical resource allocation.