Cosmic strings play a crucial role in enhancing our understanding of the fundamental structure and evolution of the Universe, unifying our knowledge of cosmology, and potentially unveiling new physical laws and phenomena. The advent and operation of space-based detectors provide an important opportunity for detecting stochastic gravitational wave backgrounds (SGWBs) generated by cosmic strings. However, the intricate nature of an SGWB poses a formidable challenge in distinguishing its signal from the complex noise and other SGWB sources by some traditional methods. Therefore, we attempt to identify SGWBs based on machine learning. Our findings show that the joint detection of Linear Symbols to Attention and Taiji significantly outperforms individual detectors, and even in the presence of numerous low SNR signals. The true positive rate remains exceptionally high with 95%. Our study demonstrates that the multiband joint analysis method significantly enhances the discernment capability for SGWBs from different origins, providing a novel technical approach to disentangle the components in composite SGWB signals. Although our discussion is based solely on simulated data, the relevant methods can provide data-driven analytical capabilities for future observations of SGWBs.

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