In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management.