# Named Entity Recognition Method Based on Multi-Feature Fusion
## 1 Introduction  
In the digital age, User-Generated Content (UGC) has become indispensable and important content in various research fields. According to the 53 rd Statistical Report on the Development of China's Internet released by CNNIC in 2023, all kinds of internet applications in China continue to deepen, and the scale of users continues to grow, pushing the proportion of \"Individuals Using the Internet\" to 90.6% [1]. The rapid development of social media platforms has provided users with a space to share personal experiences and express their opinions. These online comments and shares contain highly valuable user views and user experience feedback. How to accurately extract such information from a large number of redundant comments and conduct in-depth analysis can not only greatly enrich the sources of information for users but also greatly reduce the number of users who have access to the internet. It also provides real feedback and actual needs of consumers for products and services for relevant businesses and enterprises. However, alongside the development of the data age, which has facilitated the convenient publishing and retrieval of information, there has also emerged an increasingly challenging task of information processing. With the explosive growth of data today, extracting valuable information from vast amounts of online reviews has become a criticalissue across various fields. To effectively identify key entity information within large-scale text data, Named Entity Recognition (NER) technology has emerged as a solution. Named Entity Recognition (NER), also known as entity extraction, is designed to identify predefined textual entities from unstructured data [2]. In recent years, with the rapid development of deep learning technology, NER has made significant progress, especially methods based on pre-trained language models (such as BERT, RoBERTa, GPT, etc.). These models can effectively model through self-attention mechanisms and long-distance dependencies, thereby achieving unprecedented performance improvements in NER tasks, showing excellent capabilities in capturing contextual information and processing complex language phenomena [3]. In addition, research on cross-domain, multi-language and small sample scenarios has also made important breakthroughs. For example, through technical means such as transfer learning, few-sample learning and unsupervised pre-training, NER still has strong robustness and generalization capabilities in resource-scar scenarios. For example, [PERSON] et al. used transfer learning methods to transfer cross-domain entity feature information from the source domain to the target domain to improve the performance of the target task. The results showed that the model is more effective than the original model [4]. In this case, this technique provides crucial data support for subsequent tasks, such as knowledge graph construction, information retrieval, sentiment analysis and semantic analysis. It is one of the key research areas in the field of information extraction today. However, in the Web 2.0 era, every user has the potential to become a producer of information. The low barrier to entry for content creation and the highly convenient process of information dissemination have led to a massive influx of data. At the same time, this has resulted in information that is often inconsistent and unstructured, which can complicate feature extraction and fusion, leading to the loss of critical information. These challenges pose significant difficulties in the entity extraction process. Therefore, minimizing the potential loss of key information during feature extraction and fusion and improving the accuracy and effectiveness of entity extraction technology are crucial for making optimal use of tourist review data. To address the aforementioned challenges, this paper proposes a combined model that integrates topic analysis with Named Entity Recognition (NER) tasks, introducing an entity extraction model based on topic clustering.