Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model based on dual-flow features complementary is proposed for obtaining efficient feature information after text coding. Firstly, sentences are embedded at both the word level and the character level of the words, then sentence context information is obtained separately via the neural network Bi-LSTM; Afterward, two vectors perform low-level feature complementary to reinforce low-level semantic information; Sentence-local information is captured with the multi-head attention mechanism, then the feature vector is sent to the high-level feature complementary module to obtain deep semantic information; Finally, the entity word recognition module and the fine-grained division module are entered to obtain the internal entity. The experimental results show that the model has a great improvement in feature extraction compared to the classical model.