Cardiac　arrhythmia　is　an　abnormal　rhythm　of　the　heartbeat　and　can　be　life-threatening　Electrocardiogram　(ECG)　is　a　technology　that　uses　an　electrocardiograph　machine　to　record　a　graph　of　the　changes　in　electrical　activity　produced　by　the　heart　at　each　cardiac　cycle.　ECG　can　generally　be　used　to　check　whether　the　examinee　has　arrhythmia,　ion　channel　disease,　cardiomyopathy,　electrolyte　disorder　and　other　diseases.　To　reduce　the　workload　of　doctors　and　improve　the　accuracy　of　ECG　signal　recognition,　a　novel　and　lightweight　automatic　ECG　classification　method　based　on　Convolutional　Neural　Network　(CNN)　is　proposed.　The　multi-branch　network　with　different　receptive　fields　is　used　to　extract　the　multi-spatial　deep　features　of　heartbeats.　The　Channel　Attention　Module　(CAM)　and　Bidirectional　Long　Short-Term　Memory　neural　network　(BLSTM)　module　are　used　to　filter　redundant　ECG　features.　CAM　and　BLSTM　are　beneficial　for　distinguishing　different　categories　of　heartbeats.　In　the　experiments,　a　four-fold　cross-validation　technique　is　used　to　improve　the　generalization　capability　of　the　network,　and　it　shows　good　performance　on　the　testing　set.　This　method　divides　heartbeats　into　five　categories　according　to　the　American　Advancement　of　Medical　Instrumentation　(AAMI)　criteria,　which　is　validated　in　the　MIT-BIH　arrhythmia　database.　The　sensitivity　of　this　method　to　Ventricular　Ectopic　Beat　(VEB)　is　98.5%　and　the　F1　score　is　98.2%.　The　precision　of　the　Supraventricular　Ectopic　Beat　(SVEB)　is　91.1%,　and　the　corresponding　F1　score　is　90.8%.　The　proposed　method　has　high　classification　performance　and　a　lightweight　feature.　In　a　word,　it　has　broad　application　prospects　in　clinical　medicine　and　health　testing.　©　2023　ISA