Label noise can greatly reduce the performance of deep network models. To address this problem, this study proposes a contrastive learning-based label noisy image classification method. The method includes an adaptive threshold, contrastive learning module, and class prototype-based label denoising module. Firstly, the robust features of the image are extracted by maximizing the similarity between two augmented views of the same image using contrastive learning. Then, a novel adaptive threshold filtering training sample is used to dynamically adjust the threshold based on the learning status of each class during model training. Finally, a class prototype-based label denoising module is introduced to update pseudo-labels by calculating the similarity between sample feature vectors and prototype vectors, thus avoiding the influence of label noise. Comparative experiments are conducted on the publicly available datasets CIFAR-10 and CIFAR-100 and the real dataset ANIMAL10. The experimental results show that under the condition of artificially synthesized noise, the proposed method outperforms conventional methods. By updating pseudo-labels based on the similarity between the robust feature vector of the image and various prototype vectors, the negative impact of noisy labels is reduced, and the anti-noise ability of the model is improved to certain extent, verifying the effectiveness of the proposed model.