The　study　of　style　identification　in　3D　animation　not　only　enhances　the　artistry　of　animation　scenes　but　also　has　a　profound　significance　on　the　development　of　3D　animation　works.　In　this　paper,　we　first　sorted　out　the　art　style　expressions　of　3D　animation　and　explored　the　innovation　path　of　3D　animation　art　style　and　related　influencing　factors.　Based　on　the　generative　adversarial　network　in　AIGC　technology,　the　discriminator　and　generator　are　trained.　The　3D　animation　style　intelligent　recognition　and　classification　model　is　established　by　combining　the　GAN　network　and　SoftMax　classifier,　and　the　loss　function　of　the　model　is　optimized.　Using　crawler　technology　to　obtain　3D　animation-related　images　and　crop　them　to　128∗128　size　for　dataset　construction,　3D　animation　style　recognition　and　classification　experiments　were　designed　to　verify　the　application　effectiveness　of　the　3D-GAN　model.　It　is　found　that　the　3D-GAN　model　reaches　the　peak　of　IS　index　9.222　after　60∗103　iterations,　which　is　12.85%　higher　than　the　ALGAN　model.　The　average　classification　accuracy　of　the　3D-GAN　model　is　91.42%,　which　is　6.01　percentage　points　higher　than　that　of　the　sub-optimal　CycleGAN　model.　Conducting　intelligent　recognition　and　classification　of　3D　animation　styles　based　on　AIGC　technology　can　provide　a　new　direction　for　innovating　3D　animation　styles　and　optimizing　scene　design.　©　2024　Xinqu　Jiang　et　al.,　published　by　Sciendo.