In order to realize efficient inspection of laser welding quality, this paper introduces linear array image sensing to address online inspection, and proposes a fast inspection method of weld defect based on deep learning. First, aiming at the laser weld defect, a deep learning network based on Yolo (You only look once) is optimized. Then, an appropriate anchor frame is added to the experimental data set to improve the accuracy of detection frame positioning information, and multi-scale feature fusion technology is used to improve the accuracy of defect recognition, Finally, the data set is made and a data set preprocessing method is proposed to train the network, which improves the recognition effect of defects. Experimental results show that the total recognition rate of single hole, perforation and groove defect is more than 94%, and the detection time of single workpiece image with size of 4096pixel×4000pixel is 0.97s, which is significantly faster than traditional ultrasonic and radiographic image detection methods.