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With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals. [ABSTRACT FROM AUTHOR]

• The model achieves faulted-classification without marking fault occurring time. • The model can classify the fault type of real-world transmission lines. • Maximum mean discrepancy is added to the objective function of CNN. • Gradient similarity-based image is used as the input of the model. Accurate faulted-phase classification for transmission lines is important to ensure the power systems security, and the machine learning-based methods were widely studied because of their strong generalization ability. However, these methods often require precise marking of fault occurring time. Also, existing methods face the challenges in real-world applications because they are trained using the laboratory samples. In this paper, we propose a novel faulted-phase classification model for the transmission lines. First, the gradient similarities among multi-channel electrical signals are converted to the proposed gradient similarity-based images (GS -images), which are used as the input of neural network. With the aid of this conversion, the fault features are more obvious and there is no need to mark the fault occurring time. Second, cross-domain adaption is introduced to the optimization objective of convolutional neural network (CNN). Through this adaption, the distribution discrepancies of the top-layer features extracted by the neural network between laboratory and real-world fault samples are reduced significantly, thereby increasing the model applicability in diagnosing the real-world faults. To validate the effectiveness of the proposed model, several state-of-the-art faulted-phase classification models are used for comparison. The results show that the proposed model is well-performed in accuracy, noise immunity and real-world applicability. [ABSTRACT FROM AUTHOR]