Transfer learning technology can use experience information to assist current tasks. It has been widely used in the fields of computer vision and speech recognition, whereas it has not made obvious achievements in the electromagnetic field. The electromagnetic environment changes quickly, and the performance of the source data or the classifier model in the new environment will be significantly degraded. Retraining not only requires a lot of data but also takes time and effort. Transfer learning technology is very related to the task of electromagnetic target recognition. Based on the measured electromagnetic target data set, this paper explores several applications of transfer learning in solving the problem of small samples of electromagnetic targets, including the transfer of similar targets and the transfer of heterogeneous targets. Experimental results show that by migrating the pre-training model to the target domain small sample recognition task, when the target domain is a similar source and there are only 20 labeled samples, the verification accuracy is increased by 25% compared with the non-transfer model and the training time is greatly shortened; when the target domain is a heterogeneous source, the training time can be less than 1/5 that of the source domain while ensuring the recognition accuracy.