"The International Classification of Diseases (ICD) provides a standardized framework for encoding diagnoses, serving critical roles in clinical scenarios. Automatic ICD coding aims to assign formalized diagnostic codes to medical records for documentation and analysis, which is challenged by an extremely large and imbalanced label space, noisy and heterogeneous clinical text,and the need for interpretability. In this paper, we propose a structured multi-class classification framework that partitions diseases into clinically coherent groups, enabling group-specific dataaugmentation and supervision. Our method combines input compression with generative and discriminative fine-tuning strategies tailored to primary and secondary diagnoses, respectively.On the CCL2025-Eval Task 8 benchmark for Chinese electronic medical records, our approach ranked first in the final evaluation."