## 1 Introduction  
With the reform of the electric power system, power grid enterprises have completed the initial construction of smart grids [1; 2; 3]. As a result, they have accumulated a substantial amount of unstructured business data [4] through the implementation of smart grid information systems. This includes information about electric power marketing [5; 6] systems. To understand this huge amount of marketing data, there is a need for classification and keyword positioning. Deep semantic relationship mining has become a hot research direction in the field of natural language processing [7; 8] and electric power marketing. Named Entity Recognition (NER) [9; 10; 11] is a fundamental task in natural language processing; its main task is to identify meaningful nouns or phrases from unstructured text and classify them. In the case of power marketing, named entity recognition is mainly used to identify entities in power marketing texts. For example, by extracting the unstructured information in the complaint opinion work order, the complaint time, faulty equipment, station line, belonging station, etc., can be understood quickly. This will enable effectively improving the efficiency of marketing personnel, as well as improving the quality of power marketing services. This will provide the basis for the construction of knowledge graph [12] or power customer service intelligent question and answer systems [13] in the field of power marketing afterwards. Different from the traditional named entities, power marketing text contains a large number of proprietary terms in the field of electricity, which has the characteristics of complexity, specificity, and a strong domain. This will enhance the difficulty of named entity recognition to a certain extent. If the accuracy rate of named entity recognition is low, it will have a serious impact on downstream tasks, so how to improve the accuracy rate of named entity recognition in power marketing field is an urgent problem to be solved. At present, NER technology in the Chinese power marketing domain mainly faces the following challenges: (1) Lack of publicly available labeled datasets. (2) Electricity entities are highly specialized and difficult to identify in comparison with general-purpose fields, and there are problems such as multiple meanings of words in electricity entities. For example, both entities \"60 kV(R)(60 kV Zhenlong Line) and \"10 kV(R)(10 kV Zhenlong Station) contain the word \"R(Zhenlong). The word \"R(Zhenlong) can mean either on the line or on the station. (3) The feature extraction of traditional models is generally limited to contextual information and annotation correctness, which is more suitable for general entity recognition datasets. Some local features specific to entity words in the power marketing domain dataset were not extracted, which had an impact on the recognition effect of this type of dataset. In order to solve the above problems, this paper proposes a new method for named entity recognition in the power marketing domain. First, word vectorization is performed on the power marketing text data using the RoBERTa pre-training model. Then, the vectors are fed into the constructed DFENN to obtain both local and global features of the text and to fuse them. The output of the RoBERTa layer of the model is used as the auxiliary classification layer and the output of the DFENN layer is used as the master classification layer. The outputs of the two layers are dynamically weighted and fused to obtain new features through the attention mechanism; they are then input into the CRF layer to obtain the most reasonable label sequence. Through experimental verification, the PMDNER model proposed in this paper can better utilize the features of entities and find the key influential information for the output more precisely, thus improving the results of entity recognition and comprehensively characterizing the semantic information of the electricity marketing text. The contributions of this paper are as follows:  
1.