The print defect detection methods based on traditional image processing technology have poor robustness and the object detection methods based on deep learning are not completely suitable for the detection tasks of print defects. In order to solve the problems above， the comparison ideas in template matching method were combined with the semantic features in deep learning， and a Deep Comparison Network （CoNet） used for the detection tasks of print defects was proposed. Firstly， the Deep Comparison Module （DCM） adopting Siamese structure was proposed to mine the semantic relationship between the detection image and the reference image through extracting and fusing the feature maps of them in the semantic space. Then， based on the feature pyramid structure with asymmetric dual channels， the Multi-scale Change Detection Module （MsCDM） was proposed to locate and classify print defects. On the public printed circuit board defect dataset DeepPCB and dataset of Lijin defects， the average values of mean Average Precision （mAP） of CoNet are 99.1% and 69.8% respectively， compared with the two baseline models Max-Pooling Group Pyramid Pooling （MP-GPP） and Change-Detection Single Shot Detector （CD-SSD）， which are increased by 0.4， 3.5 percentage points and 0.7， 2.4 percentage points respectively， and the detection accuracy of CoNet is higher. Besides， when the resolution of input image is 640×640， the average time consumption of CoNet is 35.7 ms， showing that it can absolutely meet the real-time requirements of industrial detection tasks.