Data sampling is an important manner to efficiently extract useful information from original huge datasets. In order to fit with the requirements of efficiently dealing with more and more large-scale data, a novel incremental data sampling method originated from affinity propagation method is proposed, in which two integrated algorithm strategies including hierarchical incremental processing and the dynamic weighting of data samples are introduced. The proposed method mainly can balance the computational efficiency and sampling quality very well. For hierarchical incremental processing strategy, it firstly samples data items in batches and then composites samples by hierarchical way. For dynamic weighting of data samples strategy, it dynamically re-weights the preference to retain better global consistency of possible samples on data space in the incremental sampling procedure. In the experiments, artificial datasets, UCI datasets, and image datasets are used to analyze the sampling performance. The experimental results with several compared algorithms indicate that, the proposed method can gain similar sampling quality but with notably higher computational efficiency especially for more large-scale datasets. This study further applies the new method to data augmentation task in deep learning, and the corresponding experimental results show that the proposed method performs excellently. Concretely, if basic training dataset are processed by sampling enhancement with the proposed new method, the trained model performance using similar number of training samples can be significantly improved compared to traditional data enhancement strategies.