To address the problem that image classification accuracy of Residual Network （ResNet） drops due to influence of unknown heavy-tailed noise， a Multi-distribution Heavy-Tailed Noise Adaptive ResNet （MHTNA-ResNet） model was proposed. Firstly， to reduce impact of heavy-tailed noise on final predictions， a Multi-distribution Heavy-Tailed Noise Adaptive layer （MHTNA） was designed， which created noise templates using various heavy-tailed distributions to perturb clean training data， thereby enabling ResNet to get recognition capabilities for heavy-tailed noisy images through training. Secondly， MHTNA was trained adaptively， updated noise template parameters were solved by using the maximum likelihood estimation method， and the noise templates were regenerated according to these parameters， so as to ensure that the noise is always heavy-tailedly distributed. Finally， during testing， the MHTNA was abandoned and heavy-tailed noise attacks were performed to test images， thereby evaluating the model’s capability of noise resistance. Experimental results demonstrate that compared to PRIME model， the proposed model has the classification accuracy improved by an average of 3.86， 7.10 and 5.46 percentage points， respectively， on the CIFAR10， CIFAR100 and MINI-ImageNet datasets facing heavy-tailed noise attacks. It can be seen that the proposed model can improve ResNet’s robustness against heavy-tailed noise interference effectively.