As　an　advanced　finishing　technology,　magnetic　compound　fluid　finishing　(MCFF)　is　considered　capable　of　achieving　damage-free　finishing　of　low-hardness　materials　such　as　copper　alloys　with　appropriate　finishing　parameters.　However,　ignoring　the　influence　of　the　material　removal　amount　on　the　dimensional　accuracy　while　optimizing　finishing　parameters　may　result　in　excessive　material　removal　and　a　reduction　in　the　workpiece＇s　dimensional　accuracy.　Thus,　a　novel　finishing　parameter　optimization　method　considering　dimensional　accuracy　is　proposed　in　this　paper.　Firstly,　the　MCFF　experiments　are　planned　and　carried　out　for　modeling.　Secondly,　an　MCFF　model　is　established　based　on　the　integrated　learning　theory.　The　established　model,　with　a　prediction　layer　and　a　fusion　layer,　is　a　multi-layer　neural　network　fusion　model　which　can　accurately　predict　the　polished　surface　quality　and　material　removal　amount.　Thirdly,　the　finishing　parameters　are　optimized　by　the　multi-objective　particle　swarm　optimization　algorithm,　taking　the　effect　of　material　removal　on　dimensional　accuracy　into　account.　Finally,　the　model＇s　prediction　accuracy　and　the　superiority　of　the　optimized　parameters　are　verified　through　experiments.　The　results　demonstrate　that　the　developed　model　can　predict　the　finishing　effect　correctly,　and　a　workpiece　with　high-quality　polished　surfaces　and　high　dimensional　accuracy　can　be　obtained　with　the　optimized　finishing　parameters.