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Traditional cross-media retrieval methods mainly focus on coarse-grained data that re ect global characteristics while ignoring the ne-grained descriptions of local details. Meanwhile, traditional methods cannot accurately describe the correlations between the anchor and the irrelevant data. This paper aims to solve the abovementioned problems by proposing to fuse coarse-grained and ne-grained features and a multi-margin triplet loss based on a dual-framework. (1) Framework I: A multi-grained data fusion framework based on Deep Belief Network, and (2) Framework II: A multi-modality data fusion framework based on the multi-margin triplet loss function. In Framework I, the coarse-grained and ne-grained features fused by the joint Restricted Boltzmann Machine are input into Framework II. In Framework II, we innovatively propose the multi-margin triplet loss. The data, which belong to di erent modalities and semantic categories, are stepped away from the anchor in a multi-margin way. Experimental results show that the proposed method achieves better cross-media retrieval performance than other methods with di erent datasets. Furthermore, the ablation experiments verify that our proposed multi-grained fusion strategy and the multi-margin triplet loss function are e ective.