In order to ensure the safe operation of the oil pipeline system, it is necessary to accurately determine the clogging condition of the pipeline flange, so a convolutional neural network-based oil pipeline flange clogging diagnosis method was proposed. Firstly, the vibration data of the oil pipeline flange collected by recursive map was recursively obtained as a two-dimensional image, and then the image data was inputted into adaptive deep residual network (AResNet) that integrated efficient channel attention(ECA) and efficient channel attention mechanism for training. This network was able to adaptively enhance the attention to fault-sensitive features while weakening the features that were poorly correlated with faults, thus replacing the original residual network. Finally, the test set data was fed into the model for validation. The results show that the oil pipeline flange blockage diagnostic model incorporating recurrence plot and ECA-AResNet has superior accuracy, robustness and computational performance compared to existing methods.