###### Abstract
have proposed using random forest (RF) as a classifier and combined the extracted feature parameters to identify low-order digital modulation signals, which has a good recognition effect [9]. Similarly, [PERSON] et al. proposed a decision tree-based radio signal modulation identification method, which has improved stability compared with traditional methods [10]. [PERSON] et al. proposed a modulation parameter estimation algorithm based on a generalized circular autocorrelation function. This method realizes modulation identification and parameter estimation of OFDM signals [11]. In recent years, deep learning was introduced into the research of the modulation recognition of digital signals. [PERSON] et al. proposed a modulation recognition technology combining the back propagation (BP) neural network algorithm with the decision tree, which has a high average recognition rate [12]. [PERSON] et al. achieved modulation and recognition of communication signals by constructing a deep belief network [13]. [PERSON] et al. proposed a deep learning-based modulation and recognition method for communication signals. This method solves the end-to-end signal recognition problem by designing a deep neural network model, which simplifies the tedious process of artificial feature extraction [14]. [PERSON] et al. proposed to represent the data in the form of grid topology, which is combined with CNN to complete the modulation signal classification [15]. [PERSON] et al. proposed a CNN recognition algorithm based on constellation diagrams to identify modulation modes of different signals [16]. [PERSON] proposed a modulation recognition method based on deep neural network. This method preprocesses the received signal to generate a constellation map, and uses the shape of the constellation map as the input of the deep convolutional neural network. According to the trained network, the model classifies and identifies the modulated signal [17]. In addition, [PERSON] et al. used a clustering algorithm to extract and optimize the feature parameters of the signal, trained the support vector machine through the hierarchical algorithm, improved the convergence speed and improved the recognition performance at a low SNR to achieve signal modulation recognition [18]. [PERSON] et al. proposed a modulation recognition method that uses a clustering algorithm to extract the feature parameters of a signal, and trains a neural network through a variable gradient correction algorithm to achieve modulation recognition of the signal [19]. [PERSON] proposed a fuzzy classification method based on constellation diagrams, which took the signal constellation diagram as input, and processed it through a fuzzy logic analysis and processing system to achieve modulation recognition of various digital signals [20]. The common problem of the above algorithms is that the recognition rate is low at a low SNR. Therefore, this paper introduces the idea of transfer adaptation into the InceptionResNetV2 network, called as InceptionResnetV2-TA, which fully extracts the input features through deep and parallel network structures to identify the modulation mode of digital signal, and improves the recognition accuracy of the signal in a low-SNR environment.