Interdisciplinary　research　teams　are　crucial　in　solving　complex　problems　by　providing　creative　solutions　that　single-discipline　teams　cannot　achieve.　Previous　studies　have　primarily　focused　on　the　linear　relationship　between　independent　variables　and　team　innovation　performance,　neglecting　the　non-linear　aspect.　To　address　this　gap,　this　paper　examines　the　non-linear　relationship　between　diverse　factors　and　the　innovation　performance　of　interdisciplinary　research　teams　in　artificial　intelligence.　By　utilizing　the　Classification　and　Regression　Tree　(CART)　model,　the　study　reveals　that　activity　diversity　and　interdisciplinary　research　team　innovation　performance　exhibit　a　U-shaped　relationship　in　terms　of　“novelty”　innovation　performance.　Furthermore,　this　relationship　is　influenced　by　research　interest　diversity.　Specifically,　low　research　interest　diversity　leads　to　low　innovation　performance　as　activity　diversity　increases.　Meanwhile,　research　interest　diversity　emerges　as　the　most　critical　factor　impacting　innovation　performance.　The　importance　of　member　diversity,　institutional　diversity,　and　activity　diversity　on　innovation　performance　should　not　be　ignored.　Through　decision　tree　analysis,　this　paper　extends　research　on　the　multifactor　combination,　complex　nonlinear　relationships,　and　multipath　influence　mechanism　of　team　diversity　on　interdisciplinary　research　teams’　innovation　performance.　©　Copyright　2024　for　this　paper　by　its　authors.