To realize automatic inspection of welded defects, a dynamic magneto-optical imaging non-destructive detection of weld surface and subsurface defects under alternating magnetic field excitation was researched. The welded defect magneto-optical imaging mechanism based on Faraday magneto optical effect was analyzed and employed to derive the relationship between excitation variation and dynamic magneto-optical imaging by combining with alternating magnetic field principle. The subsurface weld magneto-optical imaging feature test of low-carbon steel was investigated, verifying that the proposed method could be used to detect incomplete penetration defects of weld surface. Finally, dynamic magneto-optical image of high-strength steel weld feature was analyzed and weld defect classification model was constructed through Principal Component Analysis and Support Vector Machine (PCA-SVM) mode recognition method. The result shows that the proposed method can recognize weld features (penetration, crack, sag and perfectness) in high-strength steel weldment with the entire recognition rate of defect classification model reaches to 92.6%, subsequently the automatic inspection of weld surface and subsurface defects can be realized.