Perovskite-type　lead-free　piezoelectric　ceramics　allow　access　to　illustrious　piezoelectric　coefficients　(d(33))　through　intricate　composition　design　and　experimental　modulation.　Developing　a　swift　and　accurate　technology　for　identifying　(K,　Na)NbO3　(KNN)-based　ceramic　compositions　with　high　d(33)　in　exceedingly　large　＂compositional＂　space　will　establish　an　innovative　research　paradigm　surpassing　the　traditional　empirical　trial-and-error　method.　Herein,　we　demonstrate　an　interpretable　machine　learning　(ML)　framework　for　quick　evaluation　of　KNN-based　ceramics　with　high　d(33)　based　on　data　from　published　literature.　Specifically,　a　thorough　feature　construction　was　carried　out　from　the　global　and　local　dimensions　to　establish　tree　regression　models　with　d(33)　as　the　target　property.　Subsequently,　the　feature-property　mapping　rules　of　KNN-based　piezoelectric　ceramics　are　further　optimized　through　feature　screening.　To　intuitively　understand　the　correlation　mechanisms　between　ML　regression　targets　and　features,　the　sure　independence　screening　and　sparsifying　operator　(SISSO)　method　was　employed　to　extract　the　essential　descriptors　to　explain　d(33).　A　straightforward　descriptor,　e(NMB-MVB)&　sdot;ST/(IDA)(2),　consisting　of　only　four　easily　accessible　parameters,　can　accelerate　the　evaluation　of　a　series　of　novel　KNN-based　ceramics　with　high　d(33)　while　exhibiting　strong　theoretical　interpretability.　This　work　not　only　provides　a　tool　for　the　rapid　discovery　of　high　piezoelectric　performance　in　KNN-based　ceramics　but　also　offers　a　data-driven　route　for　the　design　of　property　descriptors　in　perovskites.