Currently,　electroencephalogram　(EEG)-based　motor　imagery　(MI)　signals　have　been　received　extensive　attention,　which　can　assist　disabled　subjects　to　control　wheelchair,　automatic　driving　and　other　activities.　However,　EEG　signals　are　easily　affected　by　some　factors,　such　as　muscle　movements,　wireless　devices,　power　line,　etc.,　resulting　in　the　low　signal-to-noise　ratios　and　the　worse　recognition　results　on　EEG　decoding.　Therefore,　it　is　crucial　to　develop　a　stable　model　for　decoding　MI-EEG　signals.　To　address　this　issue　and　further　improve　the　decoding　performance　for　MI　tasks,　a　hybrid　structure　combining　convolutional　neural　networks　and　bidirectional　long　short-term　memory　(BLSTM)　model,　namely　CBLSTM,　is　developed　in　this　study　to　handle　the　various　EEG-based　MI　tasks.　Besides,　the　attention　mechanism　(AM)　model　is　further　adopted　to　adaptively　assign　the　weight　of　EEG　vital　features　and　enhance　the　expression　which　beneficial　to　classification　for　MI　tasks.　First　of　all,　the　spatial　features　and　the　time　series　features　are　extracted　by　CBLSTM　from　preprocessed　MI-EEG　data,　respectively.　Meanwhile,　more　effective　features　information　can　be　mined　by　the　AM　model,　and　the　softmax　function　is　utilized　to　recognize　intention　categories.　Ultimately,　the　numerical　results　illustrate　that　the　model　presented　achieves　an　average　accuracy　of　98.40%　on　the　public　physioNet　dataset　and　faster　training　process　for　decoding　MI　tasks,　which　is　superior　to　some　other　advanced　models.　Ablation　experiment　performed　also　verifies　the　effectiveness　and　feasibility　of　the　developed　model.　Moreover,　the　established　network　model　provides　a　good　basis　for　the　application　of　brain-computer　interface　in　rehabilitation　medicine.