The discovery of marine magnetic anomaly stripes (magnetic stripes) represents a pivotal breakthrough in Earth sciences, providing decisive evidence for the theories of seafloor spreading and plate tectonics. The study of magnetic stripes is of significant scientific importance for understanding the formation and evolutionary mechanisms of oceanic lithosphere and dynamic processes within the Earth. However, traditional geological research primarily relies on manual identification of magnetic stripes, and faces several challenges: the complex structure and large volume of magnetic stripe data, the time-consuming and labor-intensive identification process, and the susceptibility of results to subjective factors such as the interpreter's experience, making it difficult to meet the efficiency and accuracy demands of modern Earth science research. To address these challenges, researchers have introduced advanced technologies, such as artificial intelligence and big data, to explore methods for the automatic identification of magnetic stripes. In this study, we targeted magnetic anomalies around the Shatsky Rise, a typical submarine feature in the western Pacific Ocean, and employed U-Net convolutional neural networks to achieve machine-automated identification of magnetic stripes. The approach first integrated marine magnetic survey data and magnetic stripe label data from the Shatsky Rise region to construct a high-quality training dataset. Subsequently, the U-Net convolutional neural network model was trained on this dataset to obtain machine-predicted magnetic stripe distributions, which were then compared and analyzed with manually identified results to validate the reliability and accuracy of the model. The study's primary findings are as follows: The study establishes a method for the automatic identification of marine magnetic anomaly stripes based on the U-Net convolutional neural network, using the Shatsky Rise as a demonstration area. It demonstrates that the U-Net-based method for automatic identification of magnetic stripes significantly improves identification efficiency and accuracy, and reduces subjective errors introduced by manual intervention. This method provides a new technical tool for the quantitative study of marine magnetic anomaly stripes. This research not only offers scientific insights for magnetic stripe identification in the Shatsky Rise region, but also provides technical support and reference models for applications in other similar areas. Thus, the findings are of significance for promoting the intelligent transformation of magnetic stripe interpretation.