Abstract: Drug-target binding affinity is a key metric for evaluating the strength of interaction between drugs and their targets. Currently, most drug-target affinity prediction methods focus on single-modal features of either the drug or the target, failing to fully exploit the complementary nature of multi-modal information and its potential value in enhancing prediction performance. To address this issue, we propose a drug-target affinity prediction model based on multi-modal feature fusion (MMF-DTA). The model incorporates multi-modal information for drug molecules, including molecular fingerprints, molecular graphs, and ChemBERTa pre-trained embeddings, and for target proteins, it uses protein sequences, amino acid residue contact maps, and ProtBERT pre-trained embeddings. Building upon this, the model adopts a hierarchical feature fusion architecture to enable deep interaction and fusion between the drug and target multi-modal features. Experimental results demonstrate that our model outperforms other baseline methods on the Davis and KIBA datasets, validating the effectiveness of the proposed multi-modal fusion strategy.