This study presents an integrated approach combining hyperspectral imaging (HSI) and deep learning for accurate grading of clear cell renal cell carcinoma (ccRCC). A refined preprocessing pipeline-including wavelet-based denoising and principal component analysis (PCA)-effectively enhances image quality and reduces data dimensionality. The proposed architecture utilizes a 1D convolutional neural network with attention mechanisms and a Transformer module to extract both local spectral features and global contextual information. Evaluated on a dataset of 80 ccRCC samples, the model achieves 90.32% accuracy, 89.65% sensitivity, and 90.15% specificity, outperforming several state-of-the-art models. These findings demonstrate the potential of HSI-based deep learning systems to improve diagnostic accuracy and support more precise, personalized treatment planning in renal oncology.