## 1 Introduction  
As network infrastructure continues to improve, traditional industries are moving towards networking, digitalization, and intelligence. This trend is leading to a rapid iteration in communication network technology to meet the increasing industrial capacity demands. However, behind this rapid development in network technology and infrastructure lies significant cybersecurity risks. The \"2020 Annual Overview of China's Internet Network Security Situation\" [1] focuses on describing potential network risks such as APT attacks, supply chain attacks, illegal collection of personal information, ransomware viruses, targeted delivery, and Wildcard Domain Resolution. Meanwhile, according to the \"2022 Annual Report on Network Security Vulnerability Trends\", nearly 25,000 new vulnerabilities were reported in 2022, reaching a historical high and maintaining a trend of annual growth. The overall situation has seen new changes,characterized by a surge in high-risk vulnerabilities, intensified competition in zero-day exploits, disruptions in international order due to one-sided vulnerability controls, and challenges to cyberspace rights by network hegemony. Consequently, the overall network security situation has become more complex and severe. Cyberspace is a vast and intricate information environment. In the field of cybersecurity, conventional cybersecurity solutions often rely on predefined rules or signatures to detect and defend against known threats. However, they tend to struggle against newly emerged or unknown types of attack methods. To leverage the scattered and fragmented cybersecurity-related data more effectively within the Internet and enhance capabilities in threat intelligence analysis, risk assessment, and the formulation of protective measures, researchers and engineers are now embarking on the construction of a cybersecurity knowledge graph. A framework constructed based on graph theory and technology can integrate disparate information into a structured knowledge base that is meaningful and easily queryable and analyzable. Named Entity Recognition (NER) technology plays a crucial role in this process. Through Natural Language Processing (NLP) algorithms, NER accurately identifies various entities from unstructured text, such as names of malicious software, identifiers of hacker organizations, system vulnerability codes, and other crucial pieces of information. NER technology aids in rapidly extracting valuable information from extensive textual data, transforming it into a structured format that can be further processed and analyzed. The existing research and applications of NER technology mostly concentrate on the English language context, where this field has made significant progress. In the English environment, there is a relatively well-established theoretical foundation, technical framework, and abundant data repositories. For instance, within the domain of English cybersecurity, NLP tools can effectively extract critical information such as IP addresses, names of malicious software, vulnerability identifiers, etc., and accurately map them to respective nodes in a knowledge graph. However, when attempting to transfer these advanced techniques into the Chinese language environment, various challenges arise. Firstly, issues stem from the characteristics of Chinese characters. Unlike English words that are clearly separated by spaces, Chinese characters lack fixed delimiters when written. Secondly, many Chinese characters exhibit polysemy, where a single character can possess different pronunciations and meanings based on different contexts. This complexity makes direct segmentation using spaces or simple character matching to identify entities become intricate and error prone. Moreover, traditional entity recognition methods struggle to adapt to the iterative nature of cybersecurity entities and fail to resolve issues related to semantic overlap among cybersecurity entities.