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The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to 100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.