Abstract: In the field of named entity recognition, it is difficult to obtain a large number of labeled data. To solve this problem, this paper proposes a named entity recognition algorithm based on mixed transfer learning named MT-NER. The distance between the samples was used as the criterion to balance the similarity of the samples, and the instances-based transfer learning was carried out to expand the target domain samples. A new named entity recognition network structure with finetune was established by the models-based transfer learning, and the expanded target domain data set was used to train the network. Taking the medical field as an example, experiments show that MT-NER algorithm has the best effect in entity recognition in small sample data, with an accuracy of 93.31%, a recall rate of 89.5% and a F1 value of 0.931 7. Compared with the BiLSTM-CRF model, the accuracy, recall rate and F1 value of MT-NER are improved by 6.33, 3.65 and 8.91 percentage points.