Binary　classification　problem　call　be　reformulated　as　one　optimization　problem　based　oil　support　vector　machines　and　thus　is　well　solved　by　one　recurrent　neural　network　(RNN).　Multi-category　classification　problem　in　one-step　method　is　then　decomposed　into　two　sub-optimization　problems.　In　this　paper;　we　first　modify　Hie　sub-optimization　problem　about　the　bias　so　that　its　computation　is　reduced　and　its　testing　accuracy　of　classification　is　improved.　We　then　propose　a　cooperative　recurrent　neural　network　(CRNN)　for　multiclass　support　vector　machine　learning.　The　proposed　CRNN　consists　of　two　recurrent　neural　networks　(RNNs)　and　each　optimization　problem　is　solved　by　one　of　the　two　RNNs.　The　proposed　CRNN　combines　adaptively　the　two　RNN　models　so　that　the　global　optimal　solutions　of　the　two　optimization　problems　call　be　obtained.　Furthermore,　the,　convergence　speed　of　the　proposed　CRNN　is　enhanced　by　a　scaling　technique.　Computed　results　show　the　computational　advantages　of　the　proposed　CRNN　for　multiclass　SVM　learning.