Focusing on the issue that the existing hashing image retrieval methods have weak expression ability， slow training speed， low retrieval precision， and difficulty in adapting to large-scale image retrieval， an image retrieval method based on Deep Residual Network and Iterative Quantitative Hashing （DRITQH） was proposed. Firstly， the deep residual network was used to perform multiple non-linear transformations on the image data to extract features of the image data and obtain high-dimensional feature vectors with semantic features. Then， Principal Component Analysis （PCA） was used to reduce the high-dimensional image features' dimensions. At the same time， to minimize the quantization error and obtain the best projection matrix， iterative quantization was used to binarize the generated feature vectors， the rotation matrix was updated and the data was mapped to the zero-center binary hypercube. Finally， the optimal binary hash code which was used to image retrieval in the Hamming space was obtained through performing the hash learning. Experimental results show that the retrieval precisions of DRITQH for four hash codes with different lengths on NUS-WIDE dataset are 0.789， 0.831， 0.838 and 0.846 respectively， which are 0.5， 3.8， 3.7 and 4.2 percentage points higher than those of Improved Deep Hashing Network （IDHN） respectively， and the average encoding time of the proposed method is 1 717 μs less than that of IDHN. DRITQH reduces the impact of quantization errors， improves training speed， and achieves higher retrieval performance in large-scale image retrieval.