Most cross-modal Hash methods based on deep learning learn unified Hash codes of different-modality data directly through neural networks. However, these methods ignore the factor that different scales of single-modality data contain different semantic information, which affects the data feature representation, and the importance of low-dimensional features in bridging the "heterogeneity" gap. Based on the above problems, we propose a new cross-modal Hash retrieval method (MFPMC) based on multi-scale fusion and projection matching constraint, which obtains low-dimensional features of different-modality data by designing the image multi-scale fusion network and text multi-scale fusion network. Moreover, it introduces the low-dimensional feature projection matching constraint and adversarial training to ensure the distribution consistency of low-dimensional features among different modalities. Simultaneously, low-dimensional features containing rich semantic information are used as inputs for the Hash function. Furthermore, inter-modal Hash code, intra-modal Hash code, quantization, and label-embedding losses are constructed to constrain the learning of Hash function and Hash codes to ensure the generation of discriminative discrete binary Hash codes. Experiments on two benchmark cross-modal retrieval datasets (MIRFlickr-25K and NUS-WIDE) reveal that the proposed method outperforms other methods in terms of retrieval performance.