In practical classification tasks such as image annotation and disease diagnosis， there is usually a hierarchical structural relationship between the classes in the label space of data with high dimensionality of the features. Many hierarchical feature selection algorithms have been proposed for different practical tasks， but ignoring the unknown and uncertainty of feature space. In order to solve the above problems， an online streaming feature selection algorithm OH_ReliefF based on ReliefF for hierarchical classification learning was presented. Firstly， the hierarchical relationship between classes was incorporated into the ReliefF algorithm to define a new method HF_ReliefF for calculating feature weights for hierarchical data. Then， important features were dynamically selected based on the ability of features to classify decision attributes. Finally， the dynamic redundancy analysis of features was performed based on the independence between features. Experimental results show that the proposed algorithm achieves better results in all evaluation metrics of the K-Nearest Neighbor （KNN） classifier and the Lagrangian Support Vector Machine （LSVM） classifier at least 7 percentage points improvement in accuracy when compared with five advanced online streaming feature selection algorithms.