In complex electromagnetic environments,it is difficult to identify jamming signals due to small sample size.To solve the problem,a jamming signal recognition method based on meta-learning is proposed.Firstly,the Holder coefficient of the frequency response of the jamming signal is calculated.Then,the time-frequency diagram of the jamming signal is input into the residual network,and the output is the eigenvector.Multi-modal fusion of the eigenvector with the above Holder coefficient is conducted to form a new multi-dimensional eigenvector.Finally,through meta-learning,the outputted multi-dimensional eigenvector is split into a coding vector and a covariance matrix related to the time-frequency diagram of the jamming signal to calculate the predicted value of the jamming signal,and the shortest Euclidean distance between the actual value and the predicted value is calculated to identify and classify the jamming signal.The simulation results show that the jamming signal recognition method can effectively improve the recognition rate on the 1-shot and 5-shot data sets of small sample size.