Aiming at the problem of decreased robustness of spiking neural network (SNN) caused by multiple factors, an adaptive robust spiking neural network(AR-SNN) model was proposed. The model included three modules: spiking-gated linear units(S-GLU), adaptive-topK loss (A-TopK Loss), and spiking-multilayer perceptron (S-MLP). Firstly, the gating mechanism was introduced as the preprocessing layer. By improving the gated linear unit (GLU), the number of linear layers was reduced, and the S-GLU module was constructed. Secondly, the A-TopK Loss was proposed. The average loss of the samples corresponding to the top 90% of the total loss was calculated based on the proportion of cumulative losses as the final loss. Thirdly, a self-supervised learning strategy was adopted, with the multilayer perceptron (MLP) as the decoding layer to construct the S-MLP denoising network and reconstruct the original data. Finally, the experiment was conducted on the SHD speech dataset. The results show that the S-GLU module enhances the model[DK]’s attention to key information and reduces the occurrence of misclassification. The A-TopK Loss enables the model to automatically focus on samples with large losses, improving its learning ability on complex data. S-MLP enhances the feature extraction ability of the network and demonstrates certain robustness to input disturbances in noise tests. The performance of the AR-SNN model is superior to that of the original model and other SNN models, and it can effectively improve the robustness of SNN.