The paper aims to overcome the shortcoming of high error rate in traditional machine inspection of printing defects. A printing defect detection system based on convolutional neural network was proposed. The convolution neural network which can be used in practical inspection was designed, and the hardware structure of online printing quality detection system was designed. The defect detection performance of convolutional neural network with the same structure but different training times and different learning rate parameters was compared. It was verified that convolutional neural network with learning rate below 0.01 can achieve better recognition effect, while the learning rate network is hard to be converged when the learning rate is above 0.05. The more the number of network training, the higher the accuracy, and the longer the training time correspondingly. Under the premise of meeting the requirements on rapidity and accuracy, the number of network training for defect inspection of a printed matter was determined to be 50 times, and the learning rate was 0.005. In this case, the recognition rate was 90%. Experiments show that the detection system has good defect recognition ability and high classification accuracy of defect types. The system has certain practical value.