The　electrocardiogram　(ECG)　is　a　crucial　tool　for　assessing　cardiac　health　in　humans.　Aiming　to　enhance　the　accuracy　of　ECG　signal　classification,　a　novel　approach　is　proposed　based　on　relative　position　matrix　and　deep　learning　network　information　features　for　the　classification　task　in　this　paper.　The　approach　improves　the　feature　extraction　capability　and　classification　accuracy　via　techniques　of　image　conversion　and　attention　mechanism.　In　terms　of　the　recognition　strategy,　this　paper　presents　an　image　conversion　using　relative　position　matrix　information.　This　information　is　utilized　to　describe　the　relative　spatial　relationships　between　different　waveforms,　and　the　image　identification　is　successfully　applied　to　the　Gam-Resnet18　deep　learning　network　model　with　a　transfer　learning　concept　for　classification.　Ultimately,　this　model　achieved　a　total　accuracy　of　99.30%,　an　average　positive　prediction　rate　of　98.76%,　a　sensitivity　of　98.90%,　and　a　specificity　of　99.84%　with　the　relative　position　matrix　approach.　To　evaluate　the　effectiveness　of　the　proposed　method,　different　image　conversion　techniques　are　compared　on　the　test　set.　The　experimental　results　demonstrate　that　the　relative　position　matrix　information　can　better　reflect　the　differences　between　various　types　of　arrhythmias,　thereby　improving　the　accuracy　and　stability　of　classification.