In the past two years, the large language model has set off a new wave of research in the field of natural language processing, showing the ability of general-purpose artificial intelligence, which has been widely concerned by the industry. With the rapid development of medical vertical large model, its potential in clinical application has been paid more and more attention. However, there are gaps in the diagnosis of typical medical records. In order to continuously improve the application effect of medical large model in actual clinical scenarios and accelerate the implementation of medical large model, this paper describes the specific situation of our participation in the 10th China Conference on Health Information Processing (CHIP 2024). We used the improved LoRA method for fine tuning, and then used the Chain-of-Thought method for post-processing in the reasoning stage to improve performance. The experimental results show that the F1 score of our method in the final second round of evaluation reaches 0.9356, ranking second, which effectively verifies the generalization and robustness of our method.