In order to improve the detection efficiency and accuracy of high pressure manifold damage, a new intelligent detection method for bending erosion damage based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) was proposed, and a multi-scale convolution layer was used to replace the traditional single-scale convolution layer. In the MS-1DCNN model, the original time domain signal of bending erosion damage obtained through simulation experiments was used as the input of multi-scale one-dimensional convolutional neural networks, which can solve the problem that traditional methods rely on manual feature extraction and expert knowledge. Then, the feature extraction of input signals was carried out by alternating connection of multi-scale convolution layer and pooling layer. Finally, the classification results of erosion damage of bent pipe were output through the output layer. The model test results show that the erosion damage detection method based on MS-1DCNN can effectively detect erosion damage of bent pipes, and the average detection accuracy is 99.18%. Research can provide a new approach for quantitative intelligent detection of erosion damage in high-pressure manifolds.