## 1 Introduction
A good jamming classification effect was captured. In [11], the authors combined the phase and the amplitude of the ISM band wireless jamming signal, and used them as the input of the neural network, obtaining a good classification effect. The jamming recognition algorithm based on frequency-domain feature extraction mainly takes the frequency domain sequence of the jamming signal as the input of the neural network. a Wireless Interference Recognition Transformer (WIR-Transformer) network was proposed in [12]. The authors introduced regional division; self-attention was independently calculated. This ensured the accuracy of jamming recognition, and reduced the complexity effectively at the same time. Then, the frequency-domain sequence is taken as the input of the network to complete the classification of 13 kinds of jamming signals. The jamming recognition algorithm based on transform-domain feature extraction takes the time-frequency-domain feature as the input of the neural network. The time-frequency-domain feature can reflect both the time-domain feature and the frequency-domain feature of the jamming signal, and also the characteristics of the jamming signal in multiple dimensions. The work in [13] used the jamming signal time-frequency image obtained by short-time Fourier transform as the input of the VGGNet-16 network and achieved recognition of six kinds of jamming signals under low-JNR conditions. The jamming recognition algorithm based on joint-domain feature extraction takes multiple jamming features as the input of the neural network. The work in [12] combined information of the time-frequency domain and frequency domain, and applied it to the MDN-TFI network and MDN-FS network, respectively, in order to extract the features of jamming signals. At the same time, three fusion mechanisms of CT, PT, and CA have been proposed for further improving the recognition accuracy of jamming signals. The jamming recognition algorithms mentioned above are all great but studied only in the case of a large number of samples. We considered the situation, that in reality, a large number of jamming samples is not always available. In addition, multiple nodes exist in 6G communication systems. But current jamming recognition algorithms are mostly studied on the basis of a single node. Jamming information from multiple nodes cannot be used effectively. Therefore, how to make full use of the jamming information from multiple nodes is also a very important question to be considered for the accuracy. In this article, we proposed a multi-node collaborative jamming recognition algorithm based on MAML and time-frequency analysis. The most significant contributions that we have made in this article are as follows:  
1. We introduced the MAML algorithm into the jamming recognition process, simulated a variety of single jamming signals as a training set, and gained a set of parameters through training, which can realize the recognition of compound jamming signals when the data amount of compound jamming signals is limited. 2. We used the Omniglot dataset in order to complete the expansion of the jamming signal dataset, which would make the recognition network have better scalability and generalization, therefore improving the recognition accuracy of the complex jamming signal. 3. We introduced the multi-node collaboration idea into few-shot learning compound jamming recognition. Multiple jamming recognition nodes were set up, and each individual node could complete the jamming recognition task independently. The jamming recognition results would be sent to the jamming information fusion center, and the final recognition results would then be obtained by combining the jamming recognition results of multiple nodes. This effectively improved the jamming recognition accuracy.