This article proposes a robust automatic modulation classification (AMC) model based on a new architecture of convolutional neural network (CNN). The proposed model improved the classification accuracy at very low Signal-to-Noise Ratio (SNR) and achieved near-optimal classification at high SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family