In　the　rapid　quotation　process　of　construction　projects,　low　prediction　accuracy　and　high　computational　complexity　remain　challenging　issues.　To　address　these　problems,　this　study　proposes　a　novel　method　that　integrates　the　theory　of　intuitionistic　fuzzy　sets　for　intelligent　cost　prediction.　Given　that　engineering　unit　prices　are　significantly　and　dynamically　uncertain　due　to　multi-source　heterogeneous　factors　(e.g.,　seasonal　fluctuations,　regional　differences,　and　market　volatility),　we　construct　a　transformation　mechanism　of　interval-valued　intuitionistic　fuzzy　sets.　This　mechanism　expands　the　traditional　deterministic　engineering　feature　matrix　into　an　information　matrix　that　includes　hesitation　degrees　and　membership　degrees,　thereby　achieving　a　refined　representation　of　uncertain　parameters.　On　this　basis,　we　introduce　a　multi-dimensional　similarity　matching　algorithm　to　establish　an　accurate　mapping　relationship　between　the　project　under　construction　and　the　historical　case　library.　A　weighted　correction　prediction　model　based　on　similar　cases　is　proposed　to　enhance　the　robustness　of　unit　cost　prediction　results.　Verification　through　multiple　engineering　examples　demonstrates　that　this　method　provides　a　new　prediction　paradigm　for　engineering　cost　estimation,　combining　both　theoretical　rigor　and　practical　applicability.　The　research　conclusions　offer　important　references　for　the　development　of　intelligent　cost　estimation　systems　and　the　optimization　of　engineering　decision-making.