Clear cell renal cell carcinoma grading depends on accurate nuclei segmentation and fine-grained nuclei classification, both of which are challenging in crowded pathological images. This work introduces a composite high-resolution network that first separates clustered nuclei and then performs cross-category grading-aware classification. The framework addresses inter-class similarity in nuclear appearance and improves automated ccRCC grading for pathology analysis.