With　the　rapid　development　of　artificial　intelligence　and　brain-computer　interface　technologies　in　recent　years,　emotion　recognition　based　on　Electro-encephalogram　(EEG)　data　has　become　widely　used.　In　order　to　simultaneously　extract　the　temporal　and　spatial　features　of　EEG　signals,　we　propose　a　deep　spatio-temporal　attention　network　based　on　convolutional　long　short-term　memory　network　(ConvLSTM)　for　EEG　emotion　recognition　named　CLA-Net.　Firstly,　CLA-Net　maps　one-dimensional　EEG　signals　into　two-dimensional　vectors　according　to　the　spatial　position　of　EEG　electrodes.　The　two-dimensional　vectors　are　arranged　on　the　time　axis　to　obtain　spatio-temporal　sequences　with　spatial　features.　Then,　ConvLSTM　blocks　with　residual　structure　are　used　to　extract　local　emotional　features　of　different　depths　and　scales.　The　residual　learning　strategy　is　convenient　to　integrate　spatio-temporal　cues　in　this　local　feature.　Finally,　we　send　the　extracted　new　spatio-temporal　feature　sequence　into　the　self-attention　block　to　extract　the　global　feature　weight,　and　use　softmax　to　classify　emotion.　Extensive　tests　in　SEED　and　SEED-IV　were　carried　out　in　this　work,　and　good　classification　results　have　been　achieved.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.