With the widespread popularity of social media, sarcastic expressions have become increasingly common in online communication. Accurately detecting such expressions is crucial for understanding user intentions, conducting sentiment analysis, and maintaining a healthy online environment. However, sarcasm detection faces numerous challenges, such as the subtlety of text semantics and the complexity of multi-modal information fusion. To effectively address these issues, this paper proposes a sarcasm detection method that integrates Large Language Models (LLMs) with a cross-modal adaptive attention network. In terms of text processing, the powerful language capabilities of large language models are utilized to perform in-depth semantic representation of the input text, capturing rich language patterns, contextual relationships, and implied semantic information, thereby providing high-quality textual features. Meanwhile, considering the close association between multi-modal information and text, a cross-modal adaptive attention network is constructed to achieve effective fusion of text and multi-modal data. This network dynamically focuses on key information through an adaptive attention mechanism, automatically adjusting the weights of each modality to fully leverage the complementarity between modalities. In experiments, multiple representative social media datasets are selected for validation, and the results demonstrate that, compared with traditional methods, the proposed method significantly improves evaluation metrics such as accuracy and recall.