Three-dimensional　semantic　understanding　using　only　several　multi-view　images　can　largely　reduce　the　communication　burden　on　the　network.　In　addition,　while　point　clouds　are　extensively　studied　for　3D　scene　understanding,　utilization　of　multi-view　image　data　offers　rich　visual　details　and　texture　information.　However,　challenges　persist　in　lifting　2D　semantic　features　to　3D　space　and　leveraging　language　for　segmentation.　Inspired　by　recent　advancements,　this　paper　proposes　a　method　that　combines　CLIP　features　and　SAM　masks　to　create　a　feature　field　capable　of　segmenting　objects　via　natural　language　text　across　2D　multi-view　and　3D　Gaussian　splatting.　It　offers　a　promising　function　for　extracting　3D　assets　for　game　engines　and　the　metaverse.　Our　method　involves　mask　generation　from　video　frames,　extracting　physical　scales　via　RGB　Nerf　with　masks,　and　organizing　hierarchical　information　for　semantic　comprehension.　In　the　training　process,　affinity　features　maintain　scale　properties　and　guide　CLIP　feature　generation　with　auto　weights　blending　for　semantic　robustness.　A　straightforward　3D　splatting　CLIP　feature　approach　and　canonical　text　methodology　enhance　query　robustness　across　2D　multi-view　and　3D　splatting　through　relevance　score　calculation　based　on　text　CLIP　features　for　inference.　Experimental　results　demonstrate　promising　improvements　in　semantic　understanding　of　3D　scenes.　©　2024　IEEE.