The　segmentation　of　cardiac　chambers　and　the　quantification　of　clinical　functional　metrics　in　dynamic　echo-cardiography　are　the　keys　to　the　clinical　diagnosis　of　heart　disease.　Identifying　the　end-diastolic　frames　(EDFs)　and　end-systolic　frames　(ESFs)　and　manually　segmenting　the　left　ventricle　in　the　echocardiographic　cardiac　cycle　before　obtaining　the　left　ventricular　ejection　fraction　(LVEF)　is　a　time-consuming　and　tedious　task　for　clinicians.　In　this　work,　we　proposed　a　deep　learning-based　fully　automated　echocardiographic　analysis　method.　We　pro-posed　a　multi-attention　efficient　feature　fusion　network　(MAEF-Net)　to　automatically　segment　the　left　ventricle.　Then,　EDFs　and　ESFs　in　all　cardiac　cycles　were　automatically　detected　to　compute　LVEF.　The　MAEF-Net　method　used　a　multi-attention　mechanism　to　guide　the　network　to　capture　heartbeat　features　effectively,　while　sup-pressing　noise,　and　incorporated　deep　supervision　mechanism　and　spatial　pyramid　feature　fusion　to　enhance　feature　extraction　capabilities.　The　proposed　method　was　validated　on　the　public　EchoNet-Dynamic　dataset　(n　=　1226).　The　Dice　similarity　coefficient　(DSC)　of　the　left　ventricular　segmentation　reached　(93.10　+/-　2.22)%,　and　the　mean　absolute　error　(MAE)　of　cardiac　phase　detection　was　(2.36　+/-　2.23)　frames.　The　MAE　for　predicting　LVEF　was　6.29　%.　The　proposed　method　was　also　validated　on　a　private　clinical　dataset　(n　=　22).　The　DSC　of　the　left　ventricular　segmentation　reached　(92.81　+/-　2.85)%,　and　the　MAE　of　cardiac　phase　detection　was　(2.25　+/-　2.27)　frames.　The　MAE　for　predicting　LVEF　was　5.91　%,　and　the　Pearson　correlation　coefficient　r　reached　0.96.　The　proposed　method　may　be　used　as　a　new　method　for　automatic　left　ventricular　segmentation　and　quantitative　analysis　in　two-dimensional　echocardiography.　Our　code　and　trained　models　will　be　made　available　publicly　at　https://github.com/xiaojinmao-code/MAEF-Net.