In　response　to　current　phenomenon　that　all　targets　in　incremental　learning　are　fixed　pose,this　paper　considers　a　more　rigorous　setting,　i.　e.　online　class　incremental　learning　for　multi-pose　targets,　which　innovatively　proposes　an　ignoring　pose　replay　method　to　alleviate　the　catastrophic　forgetting　in　facing　multi-pose　targets　in　online　class　incremental　learning.　Firstly,　2D/3D　targets　are　point-clouded　to　facilitate　the　extraction　of　useful　geometric　information.　Secondly,the　network　modifies　for　equivariance　based　on　the　SE　(　d)(　d　=　2,3)　group　to　enable　the　network　to　extract　richer　geometric　information,thus　reducing　the　impact　of　target　poses　on　the　model　in　each　task.　Finally,specific　samples　are　sampled　for　replay　to　mitigate　catastrophic　forgetting　based　on　loss　variation.　Experimental　results　show　that　when　facing　fixed　posture　targets　MNIST　and　CIFAR-10,final　average　accuracy　reaches　to　88%　and　42.　6%　respectively,which　is　comparable　to　the　comparison　method,and　final　average　forgetting　is　significantly　better　than　the　comparison　method,with　a　reduction　of　about　3%　and　15%　respectively.　In　the　case　of　the　multi-pose　target　RotMNIST　and　trCIFAR-10,the　proposed　method　continues　to　perform　well　in　fixedpose　targets,largely　independent　of　target　pose.　In　addition,the　performance　in　3D　datasets　ModelNet40　and　trModelNet40　remains　stable.　The　method　proposed　is　able　to　be　independent　of　the　target　pose　in　online　class　incremental　learning,　while　achieving　catastrophic　forgetting　mitigation,　with　excellent　stability　and　plasticity.