Aiming at overfitting problem caused by memory behavior of Deep Neural Networks （DNNs） on image data with noisy labels， a meta label correction method based on predictions from shallow neural networks was proposed. In this method， with the use of weakly supervised training method， a label reweighting network was set to reweight noise data， meta learning method was employed to facilitate dynamic learning of the model to noise data， and the prediction output from both deep and shallow networks was used as the pseudo labels to train the model. At the same time， the knowledge distillation algorithm was applied to allow the deep network to guide the training of the shallow networks. In this way， the memory behavior of the model was alleviated effectively and the robustness of the model was enhanced. Experiments conducted on CIFAR10/100 and Clothing1M datasets demonstrate the superiority of the proposed method over Meta Label Correction （MLC） method. Particularly， on CIFAR10 dataset with symmetrical noise ratios of 60% and 80%， the accuracy improvements are 3.49 and 1.56 percentage points respectively. Furthermore， in ablation experiments on CIFAR100 dataset with asymmetric noise ratio of 40%， at most 5.32 percentage points accuracy improvement is achieved by the proposed method over models trained without predicted labels， confirming the feasibility and effectiveness of the proposed method.