Abstract: Traditional empirical formulas and loss separation formulas cannot accurately calculate ferrite core loss under wide frequency, wide flux density, wide temperature range, and complex waveform excitations. Considering the dependence of core loss on both the local and long-term characteristics of flux density waveform and utilizing the MagNet dataset built by researchers in Princeton University, we established a large-sample core loss pre-training model based on CNN-BiLSTM. The average prediction errors of core losses are all below 3% and the 95% errors are all below 10%. The 3C90 and N87 are taken as examples, few-shot core loss datasets are established, and transfer learning method is applied to train the model. The optimal transfer learning strategy is selected and optimal source model selection method are proposed. The required training steps of transfer learning and direct training are compared. The impacts of few-shot data size and initial learning rate on the transfer learning effect are analyzed. A sample size of 1 000 is taken as an example and compared with direct training, the required training steps are reduced from 500 to 50 by adopting transfer learning. The average prediction errors of 3C90 and N87 ferrite core losses are reduced from 4.49% and 6.6% to 2.66% and 2.35% respectively. The 95 percentile prediction errors are reduced from 11.97% and 17.12% to 7.22% and 6.21%, respectively. Both the convergence speed and prediction accuracy of the model are improved. In practical engineering, only few-shot dataset is required to fine-tune the parameters in the source domain model to realize fast model solving and accurate core loss prediction.