Focused on the high computational complexity， low recognition rate under the condition of low Signal-to-Noise Ratio （SNR）， and relatively simple network structure， a signal modulation recognition method based on Convolutional Long short-term Deep Neural Network （CLDNN） was proposed. Firstly， the open-source benchmark dataset RadioML2016.10a was adopted， and In-phase/Quadrature （I/Q） data conversion was performed on it， then the obtained result was used as the network input. Secondly， the CLDNN model was constructed， which was divided into three parts， that is three-layer Convolutional Neural Network （CNN）， two-layer Long Short-Term Memory （LSTM） network， and two-layer Fully Connected Network （FCN）. Finally， the proposed model was trained and tested to obtain classification results. Experimental results show that recognition accuracy of CLDNN model increases with SNR improvement and reaches 92% with SNR bigger than 4 dB， which is higher than those of the existing single network structure models such as Residual Neural Network （RES） model， CNN model and RESidual Generative Adversarial Network （RES-GAN） model， in the modulation recognition of 11 kinds of signals at different SNR.