Multi-view fuzzy clustering has garnered growing interest for its ability to effectively model soft membership relationships among samples. However, most existing methods typically compute sample membership to cluster centers directly, lacking a learnable intermediate representation layer to refine the modeling and propagation of membership information. Furthermore, some visible-hidden collaborative clustering frameworks rely on linear matrix factorization to extract a hidden view, which fails to accurately capture the nonlinear structure of the data. Therefore, this paper proposes a visible-hidden collaborative multi-view fuzzy clustering via adaptive quadratic matrix factorization and membership propagation (VCMFC-AM). This method first extracts the hidden view through an adaptive quadratic matrix factorization mechanism, which explicitly models nonlinear data structures by constructing quadratic terms and introduces adaptive weights based on reconstruction error to fuse multi-view representations. Building on this foundation, this paper further designs an end-to-end collaborative fuzzy clustering framework that introduces learnable anchors as an intermediate representation layer, establishing a two-layer membership propagation path involving sample-anchor-cluster. Additionally, the framework integrates graph regularization constraints guided by both visible and hidden views to enhance the discriminative power of the clustering results. Extensive experimental results demonstrate that VCMFC-AM significantly outperforms existing state-of-the-art methods in terms of clustering performance. Furthermore, survival curve analysis on cancer multi-omics datasets validates the method’s potential application value in clinical prognosis prediction tasks.