Machine　learning　algorithms　advance　the　classification　of　cultural　relics　in　recent　studies.　A　part　of　the　studies　adopts　image　processing　methods　as　a　solution.　We　believe　that　the　blurring　of　the　image　limits　this　approach.　On　the　other　hand,　we　notice　the　studies　conducted　from　the　perspective　of　chemical　composition,　which　avoids　the　problem　of　blurring　images　and　shows　interpretability.　In　this　paper,　we　follow　the　direction　of　classifying　by　chemical　composition　and　aim　to　provide　our　method　with　better　interpretability.　Hence,　we　propose　a　strategy　that　combined　both　supervised　and　weakly　supervised　methods　as　the　solution.　It　visualizes　the　decision　tree　of　classifying　the　main　classes　of　cultural　relics　at　first　and　extracts　the　chemical　composition　of　the　subclasses　with　the　pseudo-labeling　according　to　limited　subclass　labels　next.　Besides　interpretability,　it　also　performs　accurately　in　subclasses　classification　by　using　the　radial　basis　function　network　and　random　forest.　Our　results　indicate　that　our　weakly　supervised　method　achieves　the　closest　reduction　to　a　finer　classification.　©　2023　SPIE.