The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view. Through a unified objective function, AW-MVNFC jointly optimizes the neutrosophic membership assignments, cluster centers, and the distributions of view and feature weights. Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods, demonstrating its effectiveness in handling the complexities of multi-view data.