Marine magnetic anomaly stripes result from the coupling of geomagnetic reversals and seafloor spreading. Their morphology and spatial distribution provide valuable evidence for reconstructing the formation and evolution of tectonic units in the oceans and therefore carry important geodynamic significance. However, conventional interpretation of magnetic stripes mainly relies on expert visual inspection and profile comparison, and in processing large-area datasets, it commonly suffers from high labor and time costs, strong subjectivity, and limited interpretive consistency. To improve processing efficiency and result objectivity, this study introduces the YOLOv8 deep-learning framework for automatic marine magnetic stripe recognition and proposes an end-to-end method that spans from magnetic anomaly grid data to classification output. The study selects two subregions in the North Atlantic and the area surrounding Shatsky Rise in the western Pacific and uses global magnetic anomaly grid data (EMAG2v3) together with high-resolution regional magnetic anomaly grid data to establish a standardized workflow that transforms raw regional magnetic anomaly grids into deep-learning input samples. Standardized magnetic anomaly image samples are generated through sliding-window slicing and uniform colormap rendering, and precise alignment with the Global Seafloor Fabric and Magnetic Lineation Data Base Project (GSFML) is achieved through geographic coordinate mapping, thereby constructing a binary classification dataset of stripe and nonstripe samples. This workflow ensures standardized sample generation and preserves the spatial correspondence between anomaly slices and geological labels. Supervised training is then conducted using the lightweight YOLOv8 classification model (YOLOv8n-cls), and the optimal weights are selected. The resulting model achieves validation accuracies of 97.58% and 96.84% in the North Atlantic and western Pacific regions, respectively, while the test-set F1-scores reach 0.98 and 0.97, demonstrating stable discrimination between stripe and nonstripe textures, high recognition accuracy, and good cross-regional transferability. In addition, the method outputs class probabilities, which are used as confidence measures for the recognition results, thereby supporting hierarchical screening and manual review. This effectively improves the processing efficiency of large-scale magnetic anomaly data, reduces subjective errors introduced by manual intervention, provides a new technical pathway for studies of seafloor spreading and tectonic evolution reconstruction, and shows potential for extension to other study areas.