This　letter　proposes　an　efficient　sensitivity-driven　stepwise　modeling　method　for　microwave　components　with　a　wide　range　of　geometrical　parameter　variations.　In　the　proposed　method,　the　Pearson　correlation　coefficient　is　explored　to　solve　the　sensitivity　analysis　difficulty　in　accurately　classifying　the　geometrical　parameters　into　high-sensitivity　parameters　and　low-sensitivity　parameters.　The　relationship　between　high-sensitivity　parameters　and　circuit　responses　is　learned　by　the　neural　network　as　the　first　modeling　step.　Then,　the　effect　of　low-sensitivity　parameters　on　the　circuit　response　is　restored　in　the　second　modeling　step　through　transfer　learning　(TL),　which　leverages　the　knowledge　gained　from　high-sensitivity　parameters.　Using　the　proposed　sensitivity-driven　stepwise　modeling　method　allows　us　to　achieve　a　much　faster　training　convergence　speed　through　effective　knowledge　transfer　and　reuse,　consequently　achieving　similar　accuracy　in　a　shorter　training　time　compared　with　existing　methods　using　the　same　data.　Two　microwave　modeling　examples　are　used　to　illustrate　the　proposed　method.