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Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-ViewCategory Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep-learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel centermemory contrastive loss, which enhances the network’s global modeling capability by comparing with representative cluster centers and diverse negative samples at Corpus-level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at View-level. Finally, a parameterfree mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at Category-level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task.