# Named Entity Recognition Method Based on Multi-Feature Fusion
## 6 Conclusions  
Named entity recognition is the core link of information extraction. For online comment text, being able to efficiently and accurately extract key information from it is of great significance for subsequent analysis and practical application. This study conducted an in-depth study on the named entity recognition problem of online review text and constructed a set of named entity recognition combination models including topic clustering. This model can derive domain-specific entity categories through topic clustering and combine multiple feature extraction methods and multi-head attention mechanisms to extract and fuse multi-level features of text. First, this paper collects text corpus of online comments based on the internet platform, summarizes the key aspects of users' attention to this field through the NMF topic clustering method and extracts the entity types that need to be extracted from it; secondly, this paper proposes a model of named entity recognition based on multi-feature fusion. After the pre-training model captures the context information, the BiLSTM and DGCNN methods with fusion residual structure and gating mechanisms are used in the feature extraction layer to extract features simultaneously, and MHA is used to fuse multiple features. Finally, the optimal sequence is obtained through CRF. Experiments show that, compared with other models, the proposed model has better performance in named entity recognition tasks and can adapt to the characteristics of online text.  
Although the proposed model has achieved high accuracy in the analysis of online comments on Chinese text, online comments are usually presented in various forms, and the deep mining of Chinese text is still insufficient. In addition, the superior performance of the proposed model has only been verified on a specific experimental dataset, and its versatility in other datasets and fields has not been fully evaluated.  
Future research will focus on multimodal information extraction, aiming to explore the practical application value and academic research potential of online comments based on information in different modalities (such as text, images, etc.). At the same time, the applicability of the model in diverse datasets and different application fields will be further verified, and optimization solutions will be proposed for the real-time processing and performance bottleneck problems faced by Named Entity Recognition (NER) in large-scale datasets. In addition, subsequent research will also focus on improving the computational efficiency of the model and deeply analyzing the relationship between entity types, opinion words and sentiment polarity extracted from text so as to more comprehensively reveal users' sentiment attitudes towards different attribute dimensions.  
Conceptualization, W.H. and X.Y.; methodology, W.H; software, X.Y.; validation, X.Y.; formal analysis, W.H. and X.Y. data curation, X.Y.; writing\(-\)original draft preparation, X.Y.; writing\(-\)review and editing, W.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China\(-\)Research on the guiding and blocking mechanism of public opinion spread from the perspective of emotion model (72271128) and Postgraduate Research & Practice Innovation Program of Jiangsu Province\(-\)Sentiment Analysis of Online Comments on Short Video Platforms (KYCX24_1097).  
Not applicable.  
Restrictions apply to the datasets: The dataset provided in this article is not readily available as the data is part of ongoing research. Requests to access the dataset should be directed to [EMAIL_ADDRESS].  
The authors declare no conflict of interest.