A communication signal modulation recognition method based on Transformer model is proposed. In the data preparation stage, a Different Symbol Rate Modulation Recognition(DSRMR) data set is constructed. In the data preprocessing stage, a method of I/Q data enhancement is proposed to meet the quantitative and diverse requirements of model training, and to enhance the generalization ability of the model. In the model construction stage, the method of slice serialization is introduced into the modulation recognition Transformer model, and it is employed to optimize the input problem of the Transformer neural network model. Experimental results prove that the communication signal modulation recognition method based on the Transformer model can obtain high-precision in signal automatic modulation recognition.