## 5 Experiments and Results Analysis
### Analysis of Results  
This paper uses the constructed named entity recognition dataset in the field of power marketing for training evaluation, and uses precision, recall and \(F1\) value as the evaluation criteria to measure the performance of the model. Meanwhile, four sets of comparison experiments are set up to verify and analyze the effectiveness of the named entity recognition method proposed in this paper for the power marketing domain. (1) Comparison of the performance of different masking strategies  
In order to verify the effectiveness of the whole word masking strategy in improving named entity recognition capability in the field of power marketing, comparative experiments were carried out on the model DFENN-CRF with no masking strategy, the model BERT-DFENN-CRF using a word-level masking strategy, and the model RoBERTa-DFENN-CRF using a whole word masking strategy. The experimental results are shown in Table 5.  
As can be seen from Table 5, the model that utilizes the whole word masking exhibits the best performance with a precision rate of 88.23%. In addition, when comparing whole word masking to word-level masking, the accuracy, recall and \(F1\) values of the model improved by 2.51%, 2.93% and 2.73%, respectively. Moreover, compared to the no masking strategy, the accuracy, recall and \(F1\) values of the model improved by 5.32%, 5.04% and 5.18%, respectively. Without using the masking strategy, the model outputs word vectors lack contextual semantic information, which makes it difficult to solve the problem of words with multiple meanings, leading to relatively poor recognition performance with a precision rate of 82.91%. By implementing the word-level masking strategy, the model randomly masks the characters during pre-training, and then allows the model to predict the masked characters, so that the encoder can retain the contextual semantic representation of each character, to some extent, which solves the problem of multiple word meanings by using the contextual information with the precision rate of 85.72%. Whole word masking is compared to word-level masking by first splitting the power marketing text and then randomly masking the words, and then allowing the model to predict the masked words, so that the model can learn the complete word-level semantic information and improve the inference and representation of Chinese semantics, thus the performance of the model is further improved with the precision rate of 88.23%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Masking Strategy** & **Precision/\%** & **Recall/\%** & **F1 Value/\%** \\ \hline No Masking & 82.91 & 81.52 & 82.21 \\ Word-level Masking & 85.72 & 83.63 & 84.66 \\ Whole Word Masking & 88.23 & 86.56 & 87.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison experiments of different masking strategies. 2. Comparison of the performance of different feature extraction methods in the middle layer of the model In order to verify the effectiveness of different feature extraction approaches in the middle layer of the model for improving the recognition of named entities in the power marketing domain, comparative experiments were conducted on RoBERTa-CRF, RoBERTa-BiLSTM-CRF, RoBERTa-IDCNN-CRF, and RoBERTa-DFENN-CRF. The results of the experiments are shown in Table 6.  
From the experimental results in Table 6, we can see that the intermediate layer has the highest \(F1\) value of 87.39% using the DFENN model. This indicates that adding a dual feature extraction neural network in the middle layer can improve the effectiveness of named entities. Model 1 achieved the lowest \(F1\) value because it did not add any intermediate layer network, and although the encoder was able to retain the contextual semantic representation of each character, the absence of an intermediate layer for global semantic information and local semantic information extraction led to the worst final results.