Signal modulation identification technology has important applications in both civilian and military fields. In the current information battlefield, due to the increasing number of information radiation sources such as various radars, communications, navigation, and electronic warfare weapons, the modulation forms are becoming more and more diverse, and the signal density is increasing, which makes the electromagnetic environment of war increasingly complicated, therefore the traditional signal modulation identification technology has been unable to adapt. A robust feature extraction, fusion and recognition technology of complex communication modulation signals is put forward, and a deep learning-based AlexNet network and complex neural network are proposed. Multimodal information in the statistical graph domain and signal I/Q waveform domain is fused for signal modulation identification. The simulation results show that the recognition accuracy of the proposed method is higher than that of the single-modal recognition method and the method without the multi-modal collaborative fusion framework under different Signal-to-Noise Ratios(SNRs).