Purpose/Significance To build an intelligent laboratory report interpretation system to help patients understand their own health conditions.Method/Process The study collects and annotates the laboratory test reports from Jintang First People’s Hospital in Sichuan province.Using ERNIE-4.0-Turbo-8K as the base model,it conducts supervised fine-tuning with the annotated data,and introduces the retrieval-augmented generation mechanism to optimize the model performance.Result/Conclusion After supervised fine-tuning,the model outperforms the pre-fine-tuning in multiple evaluation indicators for the laboratory report interpretation task.The retrieval-augmented generation mechanism indexed by item codes can accurately retrieve and match knowledge slices,improving the accuracy and interpretability of the interpretation.The optimized intelligent system based on large language models has great potential in improving the quality and efficiency of medical services.However,its accuracy and stability still need in-depth evaluation.