Multimodal recommendation aims to enhance user and item feature representations by integrating multimodal information， so as to improve recommendation performance. However， the existing methods still face challenges including insufficient cross-modal semantic information fusion， redundant multimodal features， and noise interference. To address these issues， a multimodal Recommendation method based on Semantic Fusion and Contrast Enhancement （SFCERec） was proposed. Firstly， a cross-modal semantic consistency enhancement framework was designed by which a global correlation graph was constructed through a multimodal semantic feature filtering mechanism， so as to aggregate common multimodal features dynamically while suppressing noise propagation. Concurrently， a multi-granularity attribute disentanglement module was introduced to separate coarse-grained common features from user behavior-driven fine-grained features from modal features， so as to mitigate feature redundancy. Secondly， a multi-level contrastive learning paradigm was proposed， so as to joint four tasks： cross-modal consistency alignment， user behavior similarity modeling， item semantic relevance constraint， and explicit-latent feature mutual information maximization， thereby enhancing representation discriminability through contrastive learning. Finally， a graph perturbation enhancement strategy was further incorporated， thereby employing noise injection and dual contrastive regularization to improve model robustness against sparse data and noise interference. Experimental results on Amazon-Baby， Amazon-Sports， and Amazon-Clothing datasets demonstrate that this method outperforms all baseline models in both Recall@20 and NDCG@20 metrics， particularly in sparse scenarios. Ablation studies further validate the effectiveness of the proposed method.