[Objective]In order to solve the problems of low prediction accuracy and difficult fusion of multimodal features in the existing multimodal sarcasm detection model, this paper designs an SC-attention fusion mechanism.

[Methods]The CLIP and RoBERTa models are used to extract features from three modes: picture, picture attribute and text respectively. SC-attention mechanism was combined with SENet's attention mechanism and Co-attention mechanism to fuse multi-modal features. Guided by the original modal features, attention weights are allocated reasonably. Finally, the features are input to the full connection layer for sarcasm detection.

[Results]The experimental results show that the accuracy of multimodal sarcasm detection based on SC-attention mechanism is 93.71%, and the F1 index is 91.89%. Compared with the model with the same data set, the accuracy of this model is increased by 10.27%, and the F1 value is increased by 11.5%.

[Limitations]The generalization of the model needs to be reflected in more data sets.

[Conclusions]The model proposed in this paper reduces information redundancy and feature loss, and effectively improves the accuracy of multimodal sarcasm detection.