The　State　of　Health　(SOH)　of　lithium-ion　batteries　is　crucial　for　maintaining　their　safety　and　reliability.　Electrochemical　impedance　spectroscopy　(EIS)　provides　extensive　information　about　the　battery　state,　but　the　data　across　high-,　medium-,　and　low-frequency　ranges　are　not　independent.　Practical　challenges　in　obtaining　full-　frequency　EIS　data　include　long　measurement　times,　low　accuracy　due　to　noise,　and　high　costs.　This　paper　proposes　an　SOH　estimation　method　based　on　offline　EIS　and　domain-adversarial　neural　network　(DaNN)　transfer.　Initially,　we　use　a　feature　search　strategy　on　experimental　EIS　data　to　identify　the　optimal　impedance　feature　subset　through　the　root　mean　square　error　(RMSE)　and　comprehensive　indicators.　These　features　are　then　input　into　the　DaNN　for　SOH　estimation　using　transfer　algorithms.　Finally,　based　on　feature　correlation　analysis,　weights　are　allocated　using　a　Gaussian　process　regression　model　for　a　secondary　prediction,　optimizing　the　SOH　results.　Experiments　on　laboratory　calendar　aging　and　publicly　available　cycling　aging　datasets　demonstrate　the　method＇s　effectiveness,　achieving　an　RMSE　of　＜1　%.　The　proposed　method　significantly　improves　SOH　estimation　accuracy　and　efficiency,　addressing　the　limitations　of　traditional　methods　and　reducing　dependency　on　extensive　measurement　data.　This　advancement　enhances　the　safety,　reliability,　and　cost-effectiveness　of　battery　applications.