In this paper, we propose the hybrid deep neural network-based cross-modal image and text retrieval method to explore complex cross-modal correlation by considering multi-layer learning. First, we propose intra-modal and inter-modal representations to achieve a complementary single-modal representation that preserves the correlation between the modalities. Second, we build an association between different modalities through hierarchical learning to further mine the fine-grained latent semantic association among multimodal data. The experimental results show that our algorithm substantially enhances retrieval performance and consistently outperforms four comparison methods.