## 1 Introduction
[PERSON] et al. proposed a joint classifier-demodulator-recognizer method based on the LSTM architecture [12]. This method has significant advantages in processing time-series data and improves the classification accuracy. [PERSON] et al. proposed a recognition network that combines an SNR classifier, a denoising autoencoder, and LSTM [13], which effectively enhances the model's robustness to noise and its ability to distinguish features. In general, the LSTM model captures long-sequence time domain features through a gating mechanism, effectively alleviating the overfitting problem of deep learning models and improving the signal recognition accuracy in the low SNR range. However, this method can only improve the recognition accuracy by stacking the number of LSTM layers and using denoising autoencoders, resulting in a large number of training parameters and high computational complexity. [PERSON] et al. designed a hybrid neural network using a combination of CNN and Recurrent Neural Network (RNN) for the modulation recognition of UWA signals [14]. This method effectively improves the anti-noise performance of the model. However, due to the inherent defects of RNN, it is difficult for this method to capture long-distance dependencies. [PERSON] et al. designed a Convolutional Long Short-Term Deep Neural Network (CLDNN) [15]. This method retains the strong representation ability of CNN for local TFR and introduces the long-term modeling ability of LSTM for time-series dynamics, breaking through the limitations of a single network and providing more comprehensive feature processing capabilities for signal modulation recognition. Nevertheless, CLDNN has certain limitations in terms of training speed, recognition ability for complex modulation methods, and model complexity. As the Transformer network model has been recognized for its advantages in global feature extraction through self-attention, [PERSON] et al. apply the Transformer network model to modulation recognition [16], effectively addressing the issues of excessive parameter counts and insufficient temporal dependency in similar neural networks. However, this approach is only suitable for capturing long-range dependencies and has weaker capabilities in extracting local detailed features. [PERSON] et al. proposed a CTRnet modulation recognition network by combining CNN and Transformer [17], addressing the issue that existing single Transformer network models neglect local features. [PERSON] et al. proposed a Two-Stream Transformer (TSTR) network structure by integrating CNN and Transformer to identify the modulation types of underwater acoustic signals [18]. This method primarily optimizes the self-attention mechanism and employs a soft thresholding mechanism to enhance the network's robustness to low SNR. [PERSON] et al. proposed a parallel two-branch dynamic dilated Convolutional Neural Network combined with Transformer [19]. This method adaptively adjusts the receptive field to effectively capture signal features at different scales. References [20; 21] propose a model combining the multi-head self-attention mechanism and LSTM, which effectively models long-range dependencies and reduces confusion between similar modulations. However, existing hybrid neural network architectures based on the multi-head self-attention mechanism commonly suffer from gradient degradation issues. Additionally, their fusion feature settings are often suboptimal, leading to low recognition accuracy for phase-modulated signals under low-SNR conditions, which leaves significant room for performance improvement. To address the above challenges, this paper proposes a dual-feature ResNet-Transformer (DFRT) model, with main innovations including (1) constructing a dual-modal feature fusion framework, which uses ResNet residual connections to solve the gradient degradation problem in traditional deep networks while leveraging the Transformer's multi-head self-attention mechanism to enhance long-distance dependency modeling capabilities.