Security vulnerabilities in video surveillance networks endanger public safety and even national security. Facing the continuous evolution of security threats， incremental learning methods are needed urgently. However， the existing methods suffer from classification inaccuracies in incremental learning due to three major challenges： insufficient few-shot learning performance， classification bias caused by semantic ambiguity， and limited capability to expand new categories dynamically. Therefore， an Incremental Vulnerability Classification Framework based on Large Language Model （LLM） （IVCF-LLM） was proposed. In the framework， data stratification and a dynamic threshold mechanism were employed to ensure balanced distribution of training data. In the top-level classification stage， firstly， GPT-4o was used for deep analysis to extract vulnerability trigger words from few samples， thereby generating high-quality classification prompt templates， termed as “skills”； then， the keyword extraction mechanism was optimized to identify vulnerability causes and attack methods precisely， thereby matching the optimal skill to guide GPT-3.5 Turbo for accurate classification； finally， the knowledge distillation technology was introduced to achieve seamless fusion of old and new skills， thereby realizing Class-Incremental Learning （CIL）. In the sub-layer classification stage， a Common Weakness Enumeration （CWE） knowledge graph was constructed， and static knowledge injection and dynamic relationship retrieval strategies were combined， so as to achieve fine-grained and precise classification. Experimental results demonstrate that on the self-built dataset， IVCF-LLM achieves accuracy of 75.0% and Matthews Correlation Coefficient （MCC） of 65.7%， outperforming models such as Text-to-Weakness mapping （Text2Weak）， Semantic Common weakness enumeration Predictor （SCP）， and prompt-based classification； on the general network security dataset， the accuracy of IVCF-LLM is significantly higher than that of SCP model by 15.9 percentage points， validating the proposed framework’s effectiveness and cross-scenario stability.