For the hierarchical diagnosis and treatment online collaborative diagnosis matching problem, a three-side matching decision-making method based on a cloud model is proposed to meet the service needs of hierarchical diagnosis and treatment online collaborative diagnosis matching. First, combined with domestic online medical platforms, online reviews are crawled, and BERTopic, Stanza, and Textlob are used to obtain patients’ objective matching attributes for grassroots doctors. A corresponding weight is determined by integrating attribute preference information. Second, a multi-granular probabilistic linguistic cloud model is constructed to process multi-granular probabilistic linguistic information, and a corresponding probabilistic linguistic asymmetric cloud Bonferroni Mean (PLANCBM) aggregation operator is developed. Third, considering the synergy between grassroots doctors and experts, the workload balance among doctors, and the stability of doctor-patient matching, a corresponding three-side stable matching decision-making optimization model is constructed. Finally, a practical case, sensitivity analysis and comparison analysis verify the feasibility and stability of the proposed method, providing a new theoretical idea for three-side matching in hierarchical diagnosis and treatment online medical services.