In　recent　years,　3D　point　cloud　semantic　segmentation　has　made　remarkable　progress.　However,　most　existing　work　focuses　on　designing　intricate　structures　to　aggregate　local　features,　resulting　in　a　significant　number　of　parameters　and　computational　demands.　In　this　Paper,　we　combine　the　idea　of　structural　reparameterization　in　2D　convolution　to　propose　RPNet,　which　can　effectively　reduce　the　number　of　parameters　while　fully　extracting　the　point　cloud　features.　Specifically.　We　first　design　the　multi-branch　structure　SRLFA　(Structure　Re-parameterization　Local　Feature　Abstract)　module　based　on　the　reparameterization　to　fully　extract　the　local　features　of　the　point　cloud,　and　design　the　PFA　(Point　Feature　Abstract)　module　to　extract　the　features　of　the　point　itself.　Then,　by　decoupling　the　training　and　inference　phases,　the　multi-branch　structure　is　fused　into　an　equivalent　single-branch　structure　through　the　idea　of　structural　reparameterization　during　training　and　inference,　which　ensures　the　feature　extraction　capability　while　effectively　reducing　the　number　of　parameters.　Finally,　the　proposed　method　is　trained　and　tested　on　several　public　data,　and　the　results　demonstrate　that　the　proposed　method　achieves　advanced　performance　in　mIoU　and　OA　with　effective　control　of　the　number　of　model　parameters.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2023.