Most of the existing multi-view unsupervised feature selection methods have the following problem： the similarity matrix of samples， the weight matrix of different views， and the feature weight matrix are usually predefined， and cannot effectively describe the real intrinsic structure of data and reflect the importance of different views and features， which results in the failure of selection of useful features. In order to address the above issue， firstly， adaptive learning of view weight and feature weight was performed on the basis of multi-view fuzzy C-means clustering， thereby achieving feature selection and guaranteeing the clustering performance simultaneously. Then， under the constraint of Laplacian rank， the similarity matrix of samples was learned adaptively， and an Adaptive Learning-based Multi-view Unsupervised Feature Selection （ALMUFS） method was constructed. Finally， an alternate iterative optimization algorithm was designed to solve the objective function， and the proposed method was compared with six unsupervised feature selection baseline methods on eight real datasets. Experimental results show that ALMUFS is superior to other methods in terms of clustering accuracy and F-measure. In specific， ALMUFS method improves the clustering accuracy and F-measure by 8.99 and 11.87 percentage points compared to Adaptive Collaborative Similarity Learning （ACSL） averagely and respectively and by 11.09 and 13.21 percentage points compared to Adaptive Similarity and View Weight （ASVM） averagely and respectively， which demonstrates the feasibility and effectiveness of the proposed method.