Research　on　Motor　imagery　electroencephalogram　(MI-EEG)　can　reveal　the　motor　intentions　that　trigger　electrical　changes　in　the　research　of　brain-computer　interface　(BCI),　which　enables　assistance　of　rehabilitation　treatment　of　patients　with　neurological　and　muscle　dysfunction.　Recent　deep　learning　methods　generally　fail　to　obtain　the　most　discriminative　motor　imagery　features　due　to　the　uneven　information　distribution　on　various　electrode　channels.　In　addition,　the　lack　of　sufficient　sample　sizes,　leading　to　the　limited　decoding　performance　of　neural　networks.　To　address　these　two　problems,　a　hybrid　self-attention　convolution　network　(HSACNet)　method　combined　with　optimization　method　based　on　attention　mechanism　is　proposed　in　this　paper.　HSACNet　contains　an　improved　optimization　method　which　employs　the　Particle　Swarm　Optimization　(PSO)　to　enhance　the　adaptivity　ability　of　the　model.　The　proposed　method　is　evaluated　on　BCI　Competition　IV　public　dataset　and　achieves　the　highest　average　accuracy　of　56.75　%　in　cross-subject　experiments,　which　is　significantly　higher　than　that　found　for　recently　published　algorithms.　Competitive　experimental　results　show　that　the　proposed　classification　algorithm　promotes　the　global　connection　and　the　performance　of　decoding　motor　intentions,　which　can　provide　innovative　perspectives　for　BCI　systems.　©　2023　ACM.