In　order　to　achieve　more　accurate　emotion　recognition　accuracy　from　multi-modal　bio-signal　features,　a　novel　method　to　extract　and　fuse　the　signal　with　the　stacked　auto-encoder　and　LSTM　recurrent　neural　networks　was　proposed.　The　stacked　auto-encoder　neural　network　was　used　to　compress　and　fuse　the　features.　The　deep　LSTM　recurrent　neural　network　was　employed　to　classify　the　emotion　states.　The　results　present　that　the　fused　multi-modal　features　provide　more　useful　information　than　single-modal　features.　The　deep　LSTM　recurrent　neural　network　achieves　more　accurate　emotion　classification　results　than　other　method.　The　highest　accuracy　rate　is　0.792　6　©　2017,　Editorial　Board　of　Journal　on　Communications.　All　right　reserved.