There　are　problems　of　large　average　error　and　low　classification　accuracy　in　feature　space　extraction　of　transformer　oil-paper　insulation　comprehensive　diagnosis.　These　problems　are　due　to　the　existence　of　correlation　and　redundant　features　in　high-dimensional　feature　space.　Thus　a　feature　quantity　optimization　strategy　based　on　a　fast　filtering　correlation　algorithm　and　limit　gradient　rise　is　proposed.　First,　from　the　measured　data　of　transformer　dielectric　response,　various　kinds　of　time-domain　dielectric　characteristics　are　extracted　to　form　the　initial　high-dimensional　feature　space.　Secondly,　a　two-stage　time-domain　feature　selection　method　is　proposed.　In　the　first　stage,　a　fast　correlation　filtering　algorithm　is　used　to　eliminate　the　features　with　low　correlation　and　high　redundancy,　and　in　the　second　stage　the　importance　of　features　is　evaluated　according　to　the　limit　gradient,　so　as　to　determine　the　optimal　feature　space.　Finally,　different　control　groups　are　set　for　comparative　demonstration　of　the　optimal　feature　space.　This　effectively　verifies　the　rationality　and　accuracy　of　the　optimal　feature　space　obtained　by　adopting　the　optimal　strategy　proposed　above.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.