Argument Impact Classification is intriguing but challenging to classify whether the argumentative unit or an argument is impactful in a conversation. Prior works have paid a lot of attention to the aspects of languages (i.e., linguistic features and discourse structures). However, classifying the impact or persuasiveness of an argument requires more ability and knowledge than only comprehending the semantic meaning of textual argument in a debate. Therefore, in this work, we explore whether the knowledge from ConceptNet can be utilized to enhance the Large Language Model (LLM) ’s performance in the argument impact classification tasks. In this paper, we present various knowledge representation forms and help LLMs to undertake this task. Experimental results demonstrate the effectiveness of the ConceptNet knowledge by consistently outperforming the baseline.