Due to high nesting of entities and the characteristics of long texts in the field of wind power， a nested Named Entity Recognition model based on Differential Boundary Enhancement （DBE-NER） was proposed. Firstly， a semantic encoder module was used to obtain feature representations fusing entity’s head and tail words， entity types， and relative distances， thereby enhancing the model’s ability to capture nested semantic features. Secondly， an efficient differential semantic encoding module was designed to solve the fuzziness problem of nested entity boundaries. Thirdly， a Grouped Dilated Attention Network （GDAN） was utilized to improve the model’s effectiveness in recognizing long-text entities， nested entities， and nested boundaries. Finally， the feature score matrix was input into a span decoder to obtain positions and categories of the entities. Experimental results indicate that the F1 score of DBE-NER is improved by 0.92% and 1.07% compared to those of DiFiNet （Differentiation and Filtration Network） and CNN-NER （Convolutional Neural Network for Named Entity Recognition） models on a manually annotated dataset from a large wind power energy enterprise — WPEF dataset， and the F1 scores of DBE-NER are also increased on various public datasets.