For accurate classification, a power quality disturbance recognition method of a shipboard power system based on a two dimensional residual network (2D-ResNet) is proposed. First, the one-dimensional power quality time series is transformed into a two-dimensional image by a distance matrix, and then the image is sent to the proposed 2D-ResNet to extract features. Then an output feature map is used to obtain the recognition results through the linear layer classifier to realize on-line recognition of power quality disturbances in a shipboard power system. Compared with existing feature extraction methods, this method has the highest accuracy of disturbance recognition under different signal-to-noise ratio (SNR). When the SNR is 20 dB, the average accuracy of single-label classification is 93.86%, and the average F1-score of multi-label classification is 96.52%. This proves that the 2D-ResNet can effectively extract features and is robust to noise. A single-label classifier fails to recognize unknown compound disturbance, while the multi-label classifier accurately recognizes the unknown components in the disturbance signal, and the F1-score reaches 93%, which proves that the multi-label classification is suitable for the recognition of unknown compound disturbance. This work is supported by the Shanghai Science and Technology Committee Foundation (No. 20040501200).