Multi-view Learning (MVL) is a pivotal paradigm widely adopted across various fields. Despite recent advances, significant challenges remain. Existing methods primarily focus on enhancing the performance of fused multi-view representation, often neglecting the issue of Representation Degradation (RD) arising from discrepancies in the intrinsic quality of different views. To address the limitations, we propose a novel Granular-ball Fuzzy Split and Attention Fusion (GFSAF) learning, which leverages the nature of granular-ball to extract mutual and complementary representation separately. Meanwhile, the proposed method introduces an attention variant for fused representations to mitigate the RD problem. Specifically, GFSAF mainly consists of two training stages: Split-Extract Stage (SES) and Views-Fusion Stage (VFS). First, in SES, we design a novel Granular-ball Fuzzy Contrastive Learning (GFCL) to extract mutual representation, and introduce Noise Stripping Loss (L_NS) to reduce the influence of noise for complementary representation. Next, a novel multi-head Cross Views Attention (CVA) is proposed to employ attention mechanism from multi-view perspectives for comprehensive fused representations. Experimental results on eight databases demonstrate that GFSAF achieves superior performance compared to several state-of-the-art methods. Code is available at the supplementary materials.