Aiming at the performance degradation of traditional DOA(direction of arrival) estimation algorithm under the condition of array model error, a DOA estimation algorithm based on ResNet (residual network) is proposed. According to the characteristics of data-driven neural network that it does not depend on array flow pattern, the proposed algorithm extracts features from generalized cross-correlation (GCC), and takes the extracted features as input of neural network deep classifier to classify signals. On the basis of to the classification results, the corresponding sub-interval data are selected for training and the non-linear mapping relationship between ResNet learning features and DOA estimation to form a data-driven robust DOA estimation system. The simulation and experimental results show that the proposed algorithm can effectively solve the problem that the traditional DOA algorithm cannot accurately obtain the DOA results under the condition of array model error.