The usual active learning is to sample unlabeled set by designing efficient sample information evaluation algorithms. However, information redundancy between candidate sets is often overlooked. This can cause similar data to be labeled repeatedly, producing ineffective gains for the model. In this paper, we proposed an Unsupervised Redundant Feature Elimination Active Learning module (URFEAL), which utilizes the information feature coincidence of the unlabeled set to eliminate information redundant data, thus guaranteeing the validity of each candidate data. URFEAL consists of feature clusterer and eliminator. The feature clusterer computes class boundaries based on feature densities to discretize each class of the candidate set, and the eliminator judges data similarity by overlapping degree to eliminate redundant data features. Furthermore, we propose an anti-noise sampling strategy Outlier Feature Elimination (OFE) in URFEAL to filter mislabeled sets for relabeling in the data sampling stage. We extensively evaluate our method by image classification and perform experimental validation on CIFAR-10, CIFAR-100 and CALTECH-101. The experimental results show that the improvements we make are especially significant for most existing active learning algorithms in the low data stage, which demonstrates the effectiveness and generality of URFEAL.