<p>Sarcasm is a distinct form of emotional expression, frequently utilized in everyday communication. Existing models typically treat syntactic and sentiment analysis independently, resulting in insufficient integration between the two. This paper proposes a multi-channel sarcasm recognition model that simultaneously integrates syntactic and semantic analysis. The model consists of three parallel channels: literal sentiment, which captures the direct sentiment of the text; implied sentiment, which models the latent or opposing emotions often found in sarcasm; and sentiment dependency, which examines the relationships between sentiment expressions across the text. The outputs of all three channels are combined through a fusion mechanism, providing a comprehensive representation of sarcasm in the text. This multi-dimensional approach allows the model to better capture both explicit emotional expressions and the implicit contradictions that characterize sarcasm. Empirical evaluations across several datasets demonstrate that the model achieves superior accuracy and F1 scores. The source code of this work is publicly available at: https://github.com/kexinjiang11111/multichannel-sarcasm-detection.</p>