Aiming　at　the　problem　that　the　global　search　performance　of　a　transiently　chaotic　neural　network　is　not　ideal,　a　multiple　frequency　conversion　sinusoidal　chaotic　neural　network　(MFCSCNN)　model　is　proposed　based　on　the　biological　mechanism　of　the　brain,　including　multiple　functional　modules　and　sinusoidal　signals　of　different　frequencies.　In　this　model,　multiple　FCS　functions　and　Sigmoid　functions　with　different　phase　angles　were　used　to　construct　the　excitation　function　of　neurons　in　the　form　of　weighted　sum.　In　this　paper,　the　inverted　bifurcation　diagram,　Lyapunov　exponential　diagram　and　parameter　range　of　the　model　are　given.　The　dynamic　characteristics　of　the　model　are　analyzed　and　applied　to　function　optimization　and　combinatorial　optimization　problems.　Experimental　results　show　that　the　multiple　frequency　conversion　sinusoidal　chaotic　neural　network　has　better　global　search　performance　than　the　transient　chaotic　neural　network　and　other　related　models.