SMs　(SMs),　artificial　periodic　composites　utilized　to　mitigate　seismic　hazards　by　attenuating　seismic　waves　within　specific　frequency　bands,　have　garnered　significant　research　interest　in　recent　years.　To　expedite　the　determination　of　optimal　structures　within　a　limited　design　space,　the　joint　neural　network　(JNN)　incorporating　a　Depth　Feedforward　Network　(DFN)　and　an　Undercomplete　Autoencoder　(UAE)　in　series　was　devised.　A　dual-component　SMs　dataset　was　generated　using　curve　functions　for　material　parameter　analysis.　The　UAE　was　trained　to　discern　crucial　features　of　SMs　configurations.　Dispersion　curves　for　the　sample　dataset　were　computed　using　finite　element　method　(FEM).　Employing　interval　merging　algorithm　and　normalized　frequency　labeling,　data　underwent　dimensionality　reduction　and　feature　extraction,　revealing　that　the　pre-trained　JNN　exhibited　an　error　less　than　0.2%　compared　to　FEM,　with　a　design　time　of　merely　40　s.　Adhering　to　the　principles　of　optimal　bandgaps　and　periodic　symmetry,　SMs　were　designed,　combined,　and　subjected　to　frequency　domain　analysis,　achieving　an　ultra-wide　bandgap　of　4.9-20　Hz.　Inputting　Helena　Montana-02　and　Chi-Chi　seismic　waveforms　demonstrated　reductions　in　peak　seismic　amplitudes　by　52.6%　and　72.2%　respectively,　validating　the　efficacy　of　the　design　model.