Aiming　at　the　accurate　prediction　of　the　inception　of　instability　in　a　compressor,　a　dynamic　system　stability　model　is　proposed　based　on　a　sparrow-inspired　meta-heuristic　optimization　algorithm　in　this　article.　To　achieve　this　goal,　a　spatial　mode　is　employed　for　flow　field　feature　extraction　and　modeling　object　acquisition.　The　nonlinear　characteristic　presented　in　the　system　is　addressed　using　fuzzy　entropy　as　the　identification　strategy　to　provide　a　basis　for　instability　determination.　Using　Sparrow　Search　Algorithm　(SSA)　optimization,　a　Radial　Basis　Function　Neural　Network　(RBFNN)　is　achieved　for　the　performance　prediction　of　system　status.　A　Logistic　SSA　solution　is　first　established　to　seek　the　optimal　parameters　of　the　RBFNN　to　enhance　prediction　accuracy　and　stability.　On　the　basis　of　the　RBFNN-LSSA　hybrid　model,　the　stall　inception　is　detected　about　35.8　revolutions　in　advance　using　fuzzy　entropy　identification.　To　further　improve　the　multi-step　network　model,　a　Tent　SSA　is　introduced　to　promote　the　accuracy　and　robustness　of　the　model.　A　wider　range　of　potential　solutions　within　the　TSSA　are　explored　by　incorporating　the　Tent　mapping　function.　The　TSSA-based　optimization　method　proves　a　suitable　adaptation　for　complex　nonlinear　dynamic　modeling.　And　this　method　demonstrates　superior　performance,　achieving　42　revolutions　of　advance　warning　with　multi-step　prediction.　This　RBFNN-TSSA　model　represents　a　novel　and　promising　approach　to　the　application　of　system　modeling.　These　findings　contribute　to　enhancing　the　abnormal　warning　capability　of　dynamic　systems　in　compressors.