Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from a large collection of unlabeled examples. Existing batch mode active learning algorithms suffer from three limitations: (1) the models with assumption on data are hard in finding images that are both informative and representative; (2) the methods that are based on similarity function or optimizing certain diversity measurement may lead to suboptimal performance and produce the selected set with redundant examples; (3) the problem of noise labels has been an obstacle for active learning algorithms. This study proposes a novel batch mode active learning method based on deep learning. The deep neural network generates the representations (embeddings) of labeled and unlabeled examples, and label cycle mode is adopted by connecting the embeddings from labeled examples to those of unlabeled examples and back at the same class, which considers both informativeness and representativeness of examples, as well as being robust to noisy labels. The proposed active learning method is applied to semi-supervised classification and clustering. The submodular function is designed to reduce the redundancy of the selected examples. Moreover, the query criteria of weighting losses are optimized in active learning, which automatically trade off the balance of informative and representative examples. Specifically, batch mode active scheme is incorporated into the classification approaches, in which the generalization ability is improved. For semi-supervised clustering, the proposed active scheme for constraints is used to facilitate fast convergence and perform better than unsupervised clustering. To validate the effectiveness of the proposed algorithms, extensive experiments are conducted on diversity benchmark datasets for different tasks, and the experimental results demonstrate consistent and substantial improvements over the state-of-the-art approaches.