COVID-19　has　been　rapidly　spreading　worldwide　and　infected　more　than　1　million　people　with　over　690k　deaths　reported.　It　is　urgent　and　crucial　to　identify　COVID-19-infected　patients　by　computed　tomography　(CT)　accurately　and　rapidly.　However,　we　found　that　two　problems,　weak　supervision　and　lack　of　interpretability,　hindered　its　development.　To　address　these　challenges,　we　propose　an　attention-based　multi-flow　network　for　COVID-19　classification　and　lesion　localization　from　chest　CT.　In　the　proposed　model,　we　built　a　Resnet-based　multi-flow　network　to　learn　the　local　information　and　the　longitudinal　information　from　the　full　chest　sequence　slice.　To　assist　doctors　in　decision-making,　the　attention　mechanism　integrated　into　the　network,　which　can　locate　the　key　slices　and　key　parts　from　a　full　chest　CT　sequence　of　patients.　We　have　systematically　evaluated　our　method　on　the　CT　images　of　1031　cases,　including　420　COVID-19　cases,　311CAP　cases,　and　300　non-pneumonia　cases.　Our　method　could　obtain　an　average　accuracy　of　82.3%,　with　85.7%　sensitivity　and　86.4　%　specificity,　which　outperformed　previous　works.　©　2022　IEEE.