Motivated　by　the　Bagging　Partial　Least　Squares　(Bagging　PLS)　and　Principal　Component　Analysis　(PCA)　algorithms,　a　novel　approach　known　as　Principal　Model　Analysis　(PMA)　method　is　introduced　in　this　paper.　In　the　proposed　PMA　algorithm,　the　PCA　and　the　Bagging　PLS　are　combined.　In　this　method,　multiple　PLS　models　are　trained　on　sub-training　sets,　derived　from　the　training　set　using　the　random　sampling　with　replacement　approach.　The　regression　coefficients　of　all　the　sub-PLS　models　are　fused　in　a　joint　regression　coefficient　matrix.　The　final　projection　direction　is　then　estimated　by　performing　the　PCA　on　the　joint　regression　coefficient　matrix.　Subsequently,　the　proposed　PMA　method　is　compared　with　other　traditional　dimension　reduction　methods,　such　as　PLS,　Bagging　PLS,　Linear　discriminant　analysis　(LDA)　and　PLS-LDA.　Experimental　results　on　six　public　datasets　demonstrate　that　our　proposed　method　consistently　outperforms　other　approaches　in　terms　of　classification　performance　and　exhibits　greater　stability.　Additionally,　it　is　employed　in　the　application　of　financial　statement　fraud　identification.　PMA　and　other　five　algorithms　are　utilized　to　financial　statement　fraud　which　concerned　by　the　academic　community,　and　the　results　indicate　that　the　classification　of　PMA　surpassed　that　of　the　other　methods.　©　2024,　Science　China　Press.　All　rights　reserved.