Prolonged mechanical ventilation is a major cause of poor prognosis in patients with chronic diseases and has become a significant public health issue. The risk factors for prolonged mechanical ventilation in chronic disease patients are complex and constantly changing. Due to the lack of intelligent prediction models, it is difficult to identify high-risk populations in advance and provide precise interventions, resulting in high incidence and mortality rates of prolonged mechanical ventilation among these patients. By automatically extracting and integrating multidimensional organ functional features-including respiratory, neurological, cardiac, diaphragmatic, and endocrine metrics-an adaptive, optimized predictive model is constructed to form an intelligent phenotype prediction system that balances universality with specificity. Based on this, modular and clinical phenotype-oriented intervention strategies are developed. Furthermore, by leveraging high-frequency data feedback and virtual reality technology, an intelligent respiratory rehabilitation platform is built to cover both invasive and non-invasive support. The establishment of a full-cycle clinical pathway, extending from initial respiratory support to long-term rehabilitation, creates a "monitoring-feedback-intervention" closed loop, driving the transformation of the respiratory rehabilitation system from a traditional experience-based model toward a new, data-driven, individualized, and intelligent paradigm.