With the significant increase in the complexity and diversity of modulation types in modern wireless communication environments, higher requirements are placed on the performance of automatic modulation recognition technology. This paper proposes a hybrid neural network model consisting of a convolutional neural network, a squeeze and excitation module, a long short-term memory network, a gated recurrent unit, and a fully connected layer network to improve the efficiency and accuracy of AMR technology. First, in order to address the problem of limited modulation signal recognition accuracy in low signal-to-noise ratio environments, a singular value decomposition algorithm is introduced to denoise the received I/Q signal, thereby improving the recognition accuracy of modulation signals under low signal-to-noise ratios while improving signal quality. Then, a convolutional neural network is used to extract multi-channel spatial features from the denoised signal, and then a squeeze and excitation module is added to improve the pertinence of feature extraction. The gated recurrent unit and the long short-term memory network are combined to capture the time series characteristics of the signal. Finally, the extracted features are mapped to the classification space of the modulation mode through a fully connected layer network for classification and recognition. Experimental results show that the proposed network model significantly improves the modulation recognition accuracy in a low signal-to-noise ratio environment. The average recognition accuracy on the RadioML2016.10b dataset reaches 64.63%. At the same time, it enhances and improves the distinction and recognition accuracy of QAM16 and QAM64.