Research on Software Vulnerability Information Knowledge Graph Construction Method Based on Text Mining

With the rapid development of information technology and the widespread adoption of software applications, software vulnerabilities have become a significant challenge in the field of cybersecurity. Effective processing of software vulnerability information is crucial not only for the security and reliability of information systems but also directly impacts the data security of organizations and individuals. This paper comprehensively explores the application of text mining technology and knowledge graph construction technology in the processing of software vulnerability information, aiming to improve the efficiency of vulnerability information acquisition, analysis, and management. By examining existing technologies and methods, this study focuses on the key technologies of text mining in entity recognition and relation extraction, as well as the role of knowledge graphs in integrating and managing vulnerability information. Moreover, this paper discusses the challenges faced in the big data environment and corresponding solutions, including data noise processing, entity ambiguity resolution, and high-dimensional feature processing. Through practical application case analysis, this paper demonstrates the actual effects and potential value of text mining and knowledge graph technology in enhancing the capability of software vulnerability information processing. The findings of this study not only provide new perspectives and methods for software vulnerability information processing but also offer robust technical support for cybersecurity protection.