In　scenarios　involving　coupled　excitations　from　multiple　forces,　structures　exhibit　complex　vibrational　patterns　with　superimposed　high　and　low-frequency.　This　is　particularly　evident　in　thin-walled　structures　such　as　submarine　pipelines,　where　the　coupling　of　internal　and　external　flows　leads　to　more　intricate　superimposed　vibrations　compared　to　scenarios　with　only　internal　flow　excitation.　However,　neural　networks　encounter　challenges　in　capturing　these　superimposed　vibrations　due　to　inherent　spectral　bias.　To　address　this,　the　multiple　Fourier　features　physics-informed　neural　network　(MFF-PINN)　is　proposed.　Through　multiple　Fourier　mappings　for　refined　multi-scale　and　multi-frequency　decomposition,　facilitating　PINN　in　accurately　capturing　multifrequency　superposed　vibrations.　Additionally,　the　correspondence　between　hyperparameters　and　eigenvector　frequencies　is　established,　while　the　effects　of　different　hyperparameters　and　number　of　mappings　on　the　network　is　analyzed.　The　MFF-PINN　with　multiple　mapping　decomposition　outperforms　single　mapping　in　synchronizing　the　learning　of　high　and　low-frequency,　improving　convergence　speed　and　enhancing　the　ability　to　handle　multi-frequency　superposition.　It　provides　an　effective　solution　for　modeling　and　simulating　multifrequency　superposed　problems　in　science　and　engineering.