Focusing on the issue that most unsupervised feature selection models based on dictionary learning cannot fully exploit the intrinsic correlations among data， which reduces the accuracy of feature importance judgment， an unsupervised feature selection model with Dictionary Learning and Sample Correlation Preservation （DLSCP） was proposed. Firstly， the original data were encoded by learning the dictionary atoms， and the latent representations to characterize data distribution were obtained in the dictionary space. Secondly， the intrinsic correlations among data were learned adaptively in the dictionary space to alleviate the influence of redundant and noisy features， thus obtaining accurate local structure among data. Finally， the intrinsic correlations among data were used to measure the relevance and importance of data features. Experimental results on TOX dataset show that， when selecting 50 features， DLSCP improves the Normalized Mutual Information （NMI） and clustering Accuracy （Acc） by 13.33 and 7.95 percentage points respectively compared to non negative spectral analysis model NDFS（Nonnegative Discriminative Feature Selection） and by 15.74 and 7.31 percentage points respectively compared to unsupervised feature selection model with hidden space embedding LSEUFS （Latent Space Embedding for Unsupervised Feature Selection via joint dictionary learning）， which verifies the effectiveness of DLSCP.