Combining transfer learning and reinforcement learning for optimization of online medical education

Due to the rapid updating of medical knowledge, existing online medical education systems have issues such as delayed tracking of knowledge status and poor matching of recommended content to users. To improve the medical knowledge level of learning users, this study develops an intelligent online medical education system. Then, the deep knowledge tracking technique is combined with reinforcement learning to dynamically track the user knowledge state and optimize the motion recommendation strategy. Based on this model, an improved migration learning algorithm is combined to optimize the recommendation accuracy of the model. The results indicated that compared to other models, the maximum learning efficiency value of the optimized model was 398.91 × 10 −3 . The mean square error was the smallest, at 2.11%. In the same dataset, the learning efficiency of the optimized model was 0.16 higher than that of the model using the deep knowledge tracking technique, and the initial reward value of the optimized model was 2.4 higher than that of the reinforcement learning model. The optimization model proposed in the study was effective and had good advantages in improving users’ knowledge level, which helped to enhance their learning status. The system performs well in situations with high concurrency, consumes fewer system resources, and delivers excellent performance. These features help improve the efficiency of medical education and meet users’ personalized needs. At the same time, it promotes the development of intelligent medical education and provides strong technical support for the field.