In complex communication environment, the connection between the characteristics of different signals is seldom considered in modulation recognition. A Convolutional Neural Network(CNN) is built to extract the characteristics of the time-frequency images of signals. Time-frequency transform is employed to process the one-dimensional signal into images, and image features are extracted through CNN. In order to improve the classification and recognition accuracy of the algorithm under low SNR, the texture features are also extracted from the images, and they are fused with the features extracted from the CNN. The simulation results show that the Time–Frequency Convolution Neural Network(TF–CNN) and TF–Resnet framework can achieve signal automatic modulation recognition and classification.