[Objective] This paper utilizes deep learning to recommend medical services for patients, which helps them choose doctors during online diagnosis and treatment. [Methods] First, we used the Hierarchical Attention Network and patient consultation records to construct doctor-patient models. Then, we designed doctor recommendation schemes based on the “doctor-patient” compatibility and patient “rating”. Both schemes incorporated the HAN deep learning framework to build doctor-patient models and used attention mechanisms to enhance the interaction of “doctor-patient”. Patients with similar conditions to those inquiring about treatments receive higher weights, which helped us calculate the doctor’s recommendation score. [Results] The HAN model could extract the critical information representing the patient’s condition from their disease descriptions. The recommendation hit rate was improved by 16.45% compared to the classical Word2Vec model by improving the modeling quality. For the recommendation score, the “rating” scheme based on the attention mechanism achieved the highest hit rate (79.7%), which is significantly outperforming the cosine similarity-based scheme (74.9%). [Limitations] This study only utilized historical patient consultation data under each doctor’s name to model the doctors, and the model did not include information such as the doctor’s reputation, credentials, and expertise. [Conclusions] Constructing user and recommendation objects is crucial in designing recommendation systems. Enhancing feature interaction between the users and recommendation objectives can improve recommendation quality. This study validates the advantages of deep learning modeling techniques in recommendation tasks.