Research on the Network Structure Characteristics of Doctors and the Influencing Mechanism on Recommendation Rates in Online Health Communities: A Multi-Dimensional Perspective Based on the “Good Doctor Online” Platform

(1) Background: Online health communities (OHCs) serve as ecosystems connecting doctors, patients, and medical resources. Studying their deep network structure and impact mechanisms on medical service quality provides a comprehensive understanding of digital healthcare ecosystems and has guiding significance for platform service optimization. (2) Methods: Using the “Good Doctor Online” platform as the data source, we employed social network analysis methods to construct network models from the professional title and disease-type dimensions, and used multiple linear regression statistical analysis to identify the influencing factors of doctor recommendation rates. (3) Results: Our analysis found that depression doctors exhibit the highest network connectivity (average degree = 17.378), and chief physicians demonstrate significantly higher internal connectivity (average degree = 9.353) compared to resident physicians (average degree = 0.804). The doctor recommendation rate is significantly correlated with post-consultation evaluation (r = 0.602, p < 0.001) and shows a 45% variance explanation (R2 = 0.450) in our regression model. (4) Conclusions: Different disease types in OHCs demonstrate distinct organizational patterns, with depression networks showing significantly denser connections than diabetes networks. Professional titles strongly influence network position, with chief physicians forming highly connected hubs while resident physicians remain peripheral. Recommendation rates emerge through multi-dimensional trust processes primarily driven by post-consultation evaluation quality.