## 5 Experiments and Results Analysis
### Analysis of Results
Compared with model 3, model 2 has improved all indicators, but the difference is not significant, which is because the intermediate layers of these two models can further extract semantic information. The former is better at learning the features of the whole sentence and capturing the long-distance context dependence of the text, which is better for the recognition of entities with longer length; while the latter focuses more on the information and features around the entities, which can better distinguish the entity boundaries. For example, there are some obvious features around such entities as voltage level, such as the number in front of \"KV\", and the entity type of station usually ends with \"\(\beta\)h\", so the obvious features can be captured for correct labeling. Model 4 adds a dual feature extraction neural network after the pre-trained model, which can make up for the deficiencies of BiLSTM and IDCNN, and thus achieves the best results with the precision rate of 87.39%. (3) Comparative analysis of the performance of different models To verify the performance of the PMDNER model in the recognition of named entities in the field of power marketing, comparative experiments were conducted with BiLSTM-CRF, BERT-BiLSTM-CRF, RoBERTa-BiLSTM-CRF, RoBERTa-IDCNN-CRF, RoBERTa-DFENN-CRF and RoBERTa-DFENN-Att-CRF models. The results of named entity recognition in the field of power marketing are shown in Table 7 and Figure 7. From the experimental results, it can be seen that the proposed model 7 has better recognition performance than other models, with an \(F1\) value of 88.58%. When compared to the BiLSTM-CRF benchmark model without the use of pre-trained models, the \(F1\) value has an improvement of 7.36%. After using the BERT pre-trained model, model 2 can more fully consider the location information and contextual semantic information of the characters, and improves in recognition with a 2.15% improvement in the \(F1\) value compared to model 1. Regarding the selection of pre-trained models, model 3, which utilizes RoBERTa, demonstrates further improvement with a 1.86% increase in the \(F1\) value compared to model 2. This could be attributed to the whole word masking strategy employed by RoBERTa pre-trained model, which can better characterize the Chinese semantics and is more applicable to Chinese named entities. The \(F1\) value of model 5 after using the DFENN proposed in this paper is also improved by 2.16% compared with model 3 and 2.32% compared with model 4. This is because the dual feature extraction neural network proposed in this paper can obtain the global feature information and local feature information of the input text in parallel, which makes up for the BiLSTM that only focuses on the extraction of full-text information but not local information, and also makes up for the shortcomings of IDCNN, which can only obtain local features but not long-range global features, and finally achieves good results in the named entity task in the field of power marketing. Model 6 is improved from model 5 by using the output of the RoBERTa layer as the auxiliary classification layer and the output of the DFENN layer as the master classification layer with weighted fusion using the attention mechanism function. Finally, model 6 proposed in this paper improves 0.9% compared with model 5. It is because the word vector output of the RoBERTa layer incorporates rich contextual semantic information, which can learn the global features of text and the local feature information of text after feeding it into the DFENN neural network model. Finally, the output vectors of both are used to calculate the weights by the attention mechanism function, and then after the weighted fusion can be better for the sequence annotation of power marketing data. Figure 7: Comparative experiments with different models. Model 7 is based on model 6, which introduces a focal loss function to alleviate the problem of unbalanced sample distribution.