How to migrate a source domain knowledge model with a large amount of tagged data to a target domain with a small amount of tagged data is a hot issue in few-shot learning. For the problems that the existing few-shot learning algorithm have weak generalization ability when the difference between the feature distribution of the source domain data and the target domain data is large

a few-shot learning model based on semi-supervised FSLSS (Few-Shot Learning based on Semi-Supervised) is proposed. Firstly

a relational deep learning network is established by using the pytorch framework

and the network is pre-trained by the source domain data. Then

the network is used to predict the target domain data

and the label with the highest classification probability is used as the data's pseudo label. Finally

the network is hybrid trained using the pseudo label data of the target domain and the real label data of the source domain

then repeating the pseudo-labeled and hybrid trained process. The experimental results show that the FSLSS model has better generalization ability and knowledge transfer effect than the existing few-shot learning algorithms.