Existing zero-shot learning (ZSL) models usually learn mappings between visual space and semantic space. However, few of them take the label information into account. Indirect Attribute Prediction (IAP) learns the posterior probability of each attribute by label space, but labels of seen and unseen classes are defined in different spaces, which is not suitable for Generalized ZSL (GZSL). We propose a Label-Activating Framework (LAF) for semantic-based classification. The purpose of the proposed framework is to activate the label space by learning mappings from vision and semantics to labels. In the training phase, the original label space made up of one-hot vectors is used as common space, on which visual features and semantic information are embedded. After the label space is activated, labels of unseen classes can be regarded as the linear combination of labels of seen classes. In this case, seen and unseen labels are defined in the same space, and the label space has specific meanings rather than only signs of each class. Doing so makes the activated label space become very discriminative, especially for GZSL, which is therefore more challenging and reasonable for real-world tasks. In addition, we develop a specific model based on the framework, which effectively mitigate the projection domain shift problem. Extensive experiments show our framework outperforms state-of-the-art methods and also its suitability for GZSL.