Solving　for　the　scattered　wavefield　is　a　key　scientific　problem　in　the　field　of　seismology　and　earthquake　engineering.　Physics-informed　neural　networks　(PINNs)　developed　in　recent　years　have　great　potential　in　possibly　increasing　the　flexibility　and　efficacy　of　seismic　modeling　and　inversion.　Inspired　by　self-adaptive　physics-informed　neural　networks　(SA-PINNs),　we　introduce　a　framework　for　modeling　seismic　waves　in　complex　topography　The　relevant　theoretical　model　construction　was　performed　using　the　one-dimensional　(1D)　wave　equation　as　an　example.　Using　SA-PINNs　and　combining　them　with　sparse　initial　wavefield　data　formed　by　the　spectral　element　method　(SEM),　we　carry　out　a　numerical　simulation　of　two-dimensional　(2D)　SH　wave　propagation　to　realize　typical　cases　such　as　infinite/semi-infinite　domain　and　arc-shaped　canyon/hill　topography.　For　complex　scattered　wavefields,　a　sequential　learning　method　with　time-domain　decomposition　was　introduced　in　SA-PINNs　to　improve　the　scalability　and　solution　accuracy　of　the　network.　The　accuracy　and　reliability　of　the　proposed　method　to　simulate　wave　propagation　in　complex　topography　were　verified　by　comparing　the　displacement　seismograms　calculated　by　the　SA-PINNs　method　with　those　calculated　by　the　SEM.　The　results　show　that　the　SA-PINNs　have　the　advantage　of　gridless　and　fine-grained　simulation　and　can　realize　numerical　simulation　conditions,　such　as　free　surface　and　side-boundary　wavefield　transmission.