Zero-shot learning (ZSL) aims to classify unseen classes using semantic information from seen classes. However, existing methods often struggle with visual variations within the same attribute, leading to noisy features. We propose CRAE (Class Representation and Attribute Embedding), a novel ZSL method that combines class representation learning and attribute embedding learning for improved robustness and accuracy. CRAE introduces an adaptive softmax activation to normalize attribute feature maps, reducing noise and enhancing discriminability. It also employs attribute-level contrastive learning with hard sample selection and class-level contrastive learning to improve classification performance. Experimental results on CUB, SUN, and AWA2 demonstrate that CRAE outperforms state-of-the-art methods, proving its superiority in zero-shot image classification.