With the development of social media,an increasing number of people express their opinions about hot topics on social platforms,and the utilization of sarcastic expression has severely affected the accuracy of sentiment analysis in social media.Currently,topic-oriented sarcasm detection research does not consider the role of context and common sense knowledge simultaneously,and also ignores the scene of sarcasm recognition under the same topic.This paper proposes a sarcasm detection with context and commonsense(CCSD)approach.Firstly,the model uses the C³KG commonsense knowledge base to generate commonsense text.Then,the target sentence,topic context,and commonsense text are concatenated as the input to the pre-training BERT model.In addition,an attention mechanism is used to focus on important information in the target sentence and commonsense text.Finally,sarcasm detections are realized through gating mechanism and feature fusion.A topic-oriented sarcasm detection dataset is constructed to verify the effectiveness of the proposed model in specific topics.Experimental results show that the proposed model achieves better performance compared to baseline models.