A doctor recommendation model based on multidimensional feature extraction of doctors and patients from online medical platform

To address the challenge of efficiently allocating limited medical resources in China, this study proposes a similarity-driven online doctor recommendation model (SimRec) to improve healthcare accessibility and resource utilization. The model was developed using object-oriented methods to analyze the current service mode of online consultation platforms, incorporating the actual needs of doctors and patients into its design. The framework consists of two layers: the object layer, which represents patient and doctor models abstractly, and the function layer, which implements recommendation technology. The function layer divides the process into two stages—department prediction and doctor-patient matching—to guide patients to appropriate departments, recommend suitable doctors, and allocate doctors based on patient needs. Tests on real-world data demonstrate that SimRec achieves better performance compared to baseline models in both department prediction and doctor-patient matching, indicating its effectiveness in optimizing medical resource allocation.