In respiratory sound classification, conventional self-supervised methods struggle with data scarcity and class imbalance. To address this problem, we propose a dual-branch learning framework that integrates masked reconstruction with contrastive learning. Our method employs an asymmetric encoder-decoder to reconstruct the masked patch and introduces a supervised classification head to extract and reconstruct the original spectral signals, enhancing the understanding of the global semantics of the encoder. Meanwhile, contrastive learning is utilized to strengthen the discriminative learning ability between instances. By jointly optimizing reconstruction, classification, and contrastive objectives, the framework effectively mitigates data limitations and improves generalization. Experiments on the ICBHI 2017 and SPRSound datasets demonstrate superior performance, outperforming existing self-supervised approaches. This validates the effectiveness of combining masked reconstruction with contrastive learning for the classification of respiratory sounds.