Cardiovascular　disease　is　a　significant　cause　of　global　mortality,　and　electrocardiogram　(ECG)　is　a　commonly　used　clinical　tool　for　detecting　heart　health　and　cardiovascular　disease.　Considering　the　discontinuity　of　ECG　signal　leads,　the　long　temporal　nature　within　leads,　and　the　intermittency　of　disease　outbreaks.　This　article　proposes　an　ECG　classification　model　that　integrates　Pearson　related　beats.　In　response　to　the　intermittent　characteristics　of　ECG　signal　disease　outbreaks,　this　model　calculates　the　Pearson　correlation　between　ECG　segments　to　obtain　the　least　correlated　segment　in　ECG　data,　in　order　to　find　a　special　rhythm　(possible　onset　rhythm);　Secondly,　in　order　to　enhance　the　features　of　special　beats,　the　model　uses　a　parallel　based　hole　Unet　to　fuse　special　beats　with　raw　data　to　obtain　enhanced　local　features　of　the　data;　In　response　to　the　long　temporal　characteristics　of　ECG　signals,　this　model　uses　a　Transformer　model　that　integrates　local　feature　prediction　to　obtain　the　long　sequence　features　of　ECG.　We　evaluated　the　proposed　method　on　the　CPSC2018　dataset　and　PTB-XL　dataset,　where　the　average　F1　for　9　types　of　arrhythmia　diseases　on　the　CPSC2018　dataset　was　0.84　and　the　average　F1　for　5　super　types　and　23　sub　types　of　diseases　on　the　PTB-XL　dataset　were　0.812　and　0.486　respectively.　　©　1963-2012　IEEE.