Left　ventricular　ejection　fraction　is　one　of　the　important　indices　to　evaluate　cardiac　function.　Manual　segmentation　of　the　left　ventricle　(LV)　in　2-D　echocardiograms　is　tedious　and　time-consuming.　We　proposed　a　deep　learning　method　called　convolutional　long-short-term-memory　attention-gated　U-Net　(CLA-U-Net)　for　automatic　segmentation　of　the　LV　in　2-D　echocardiograms.　The　CLA-U-Net　model　was　trained　and　tested　using　the　EchoNet-Dynamic　dataset.　The　dataset　contained　9984　annotated　echocardiogram　videos　(training　set:　7456;　validation　set:　1296;　test　set　1232).　The　model　was　also　tested　on　a　private　clinical　dataset　of　20　echocardiogram　videos.　U-Net　was　used　as　the　basic　encoder　and　decoder　structure,　and　some　very　useful　structures　were　designed.　In　the　encoding　part,　we　incorporated　a　convolutional　long-short-term-memory　(C-LSTM)　block　to　guide　the　network　to　capture　the　temporal　information　between　frames　in　the　videos.　In　addition,　we　replaced　the　skip-connection　structure　of　the　original　U-Net　with　a　channel　attention　mechanism,　which　can　amplify　the　desired　feature　signals　and　suppress　the　noise.　With　the　proposed　CLA-U-Net,　the　LV　was　segmented　automatically　on　the　EchoNet-Dynamic　test　set,　and　a　Dice　similarity　coefficient　(DSC)　of　0.9311　was　obtained.　The　DSC　obtained　by　the　DeepLabV3　network　was　0.9236.　The　hyperparameters　of　CLA-U-Net　were　only　19.9　MB,　reduced　by　similar　to　91.6%　as　compared　with　DeepLabV3　network　For　the　private　clinical　dataset,　a　DSC　of　0.9192　was　obtained.　Our　CLA-U-Net　achieved　a　desirable　LV　segmentation　accuracy,　with　a　lower　amount　of　hyperparameters.　The　CLA-U-Net　may　be　used　as　a　new　lightweight　deep　learning　method　for　automatic　LV　segmentation　in　2-D　echocardiograms.