Lack　of　shape　guidance　and　label　jitter　caused　by　information　deficiency　of　weak　label　are　the　main　problems　in　3D　weakly-supervised　object　detection.　Current　weakly-supervised　models　often　use　heuristics　or　assumptions　methods　to　infer　information　from　weak　labels　without　taking　advantage　of　the　inherent　clues　of　weakly-supervised　and　fully-supervised　methods,　thus　it　is　difficult　to　explore　a　method　that　combines　data　utilization　efficiency　and　model　accuracy.　In　an　attempt　to　address　these　issues,　we　propose　a　novel　plug-and-in　point　cloud　feature　representation　network　called　Multi-scale　Mixed　Attention　(MMA).　MMA　utilizes　adjacency　attention　within　neighborhoods　and　disparity　attention　at　different　density　scales　to　build　a　feature　representation　network.　The　smart　feature　representation　obtained　from　MMA　has　shape　tendency　and　object　existence　area　inference,　which　can　constrain　the　region　of　the　detection　boxes,　thereby　alleviating　the　problems　caused　by　the　information　default　of　weak　labels.　Extensive　experiments　show　that　in　indoor　weak　label　scenarios,　the　fully-supervised　network　can　perform　close　to　that　of　the　weakly-supervised　network　merely　through　the　improvement　of　point　feature　by　MMA.　At　the　same　time,　MMA　can　turn　waste　into　treasure,　reversing　the　label　jitter　problem　that　originally　interfered　with　weakly-supervised　detection　into　the　source　of　data　enhancement,　strengthening　the　performance　of　existing　weak　supervision　detection　methods.　Our　code　is　available　at　https://github.com/hzx-9894/MMA.　©　2024　IEEE.