Segmentation　of　cardiac　chambers　and　quantification　of　clinical　functional　parameters　in　dynamic　echocardiography　are　the　key　to　clinical　diagnosis　of　heart　disease.　It　is　challenging　and　tedious　for　clinicians　to　identify　end-diastolic　frames　(EDFs)　and　end-systolic　frames　(ESFs)　in　echocardiogram　cine　loops　and　to　manually　segment　the　left　ventricle　(LV).　In　this　work,　we　presented　a　new　multi-attention　efficient　feature　fusion　network　(MAEF-Net)　for　automatic　identification　of　EDFs　and　ESFs　in　echocardiogram　videos　and　for　automatic　segmentation　of　the　LV.　Multi-attention　mechanism　was　incorporated　to　guide　the　network　to　capture　the　characteristics　of　heart　beat　more　efficiently,　even　in　low-quality　videos.　At　the　same　time,　the　deep　supervision　mechanism　and　spatial　pyramid　feature　fusion　were　used　to　enhance　the　ability　of　feature　extraction　and　gradient　flow　information　was　used　to　accurately　segment　the　region　of　interest.　MAEF-Net　was　trained　and　tested　using　the　large　public　EchoNet-Dynamic　data　set.　The　Dice　similarity　coefficient　of　LV　segmentation　reached　(93.10　+/-　2.22)%.　The　key　frame　positioning　error　was　(2.80　+/-　0.22)　frames.　By　using　the　information　across　multiple　cardiac　cycles,　our　model　is　more　repeatable　and　robust　with　clinical　interpretability.　The　proposed　MAEF-Net　may　be　used　as　a　new　method　for　automatic　segmentation　of　the　LV　and　identification　of　EDFs　and　ESFs.