## 2 Related Work
[22] used a pre-trained XLNet model to extract sentence features, and then combined it with BiLSTM and CRF to demonstrate the superiority of XLNet in the NER task, which resulted in good recognition results on both the CoNLL-2003 English dataset and WNUT-2017 dataset. [PERSON] et al. from the Google team [23] proposed a language pre-trained model based on Bidirectional Encoder Representation from Transformers (BERT) to characterize word vectors in 2018. [PERSON] et al. [24] proposed a novel model, BERT-BiLSTM-CRF, designed specifically for identifying and extracting power equipment entities from pre-processed Chinese technical literature. [PERSON] et al. [25] proposed a new strategy to incorporate dictionary features into the model, and then pre-trained the model on unlabeled clinical data using BERT, which can exploit unlabeled domain-specific knowledge, and then extracted text features and predictive labels using long short-term mem ory (LSTM) and CRF. [PERSON] et al. [26] employed the RoBERTa-BiLSTM-CRF model for clinical named entity recognition (CNER) and achieved favorable recognition results on the CCKS2017 and CCKS2019 datasets. [PERSON] et al. [27] used BioBERT, a domain-specific language representation model that had been pre-trained on a large-scale biomedical corpus, for named entity recognition, with boosting results substantially better than BERT and previous state-of-the-art models. [PERSON] et al. [28] proposed an entity recognition method based on progressive multi-type feature fusion. This approach utilized the BERT preprocessing model to obtain word vectors with contextual information and conducted named entity recognition on power maintenance datasets. [PERSON] et al. [29] proposed a named entity method for power communication planning based on Transformer and BiLSTM-CRF models to address challenges associated with long text and low efficiency of information extraction in power communication planning reports. The studies mentioned above primarily focused on single word vector features for named entity recognition, overlooking the domain-specific text in an integrated manner. In modeling, the model tends to prioritize global features of text sequences, neglecting the modeling of local semantic information within the text sequences. However, the inclusion of local semantic information plays an equally critical role in the recognition of named entities, and ignoring it may lead to sparse and incomplete feature extraction, ultimately leading to lower accuracy in the named entity model. In this paper, simultaneous extraction of global and local features is proposed for named entity recognition in the electric power marketing domain. This approach ensures that local features are adequately considered alongside global features when text sequences are extracted. Through the attention mechanism, the word vector output from the RoBERTa layer and the features extracted from the DFENN layer are dynamically weighted and fused, which helps in better utilization of entity features and addresses challenges such as low utilization of entity features, multiple meanings of words and inadequate recognition of specialized terms in the entity recognition process.