To　better　simulate　the　irrational　decision-making　behavior　of　electric　vehicle　(EV),　this　paper　proposes　an　innovative　EV　charging　navigation　strategy　integrating　user　regret　psychology　and　deep　reinforcement　learning　considering　multiple　uncertain　factors.　Firstly,　a　mathematical　optimization　model　for　EV　charging　navigation　based　on　user　regret　psychology　is　established.　It　calculates　the　regret　value　of　strategy　by　time　and　charging　cost　based　on　historical　data.　Then,　the　proposed　model　is　optimized　by　the　Double　Deep　Q　Network　(DDQN)　reinforcement　learning　algorithm　to　minimize　the　regret　value　of　the　strategy.　Finally,　the　proposed　method　is　simulated　in　an　urban　traffic　network.　The　simulation　results　show　that　the　proposed　method　can　effectively　solve　problems　with　multiple　uncertainties.　Compared　with　the　nearest　charging　strategy　based　on　the　Dijkstra　algorithm,　the　proposed　strategy　can　effectively　reduce　the　regret　value,　thereby　lowering　the　total　cost　of　EV　charging,　and　it　is　more　acceptable　to　user.　Furthermore,　the　proposed　strategy　maintains　good　adaptability　and　effectiveness　in　different　simulation　environments.　©　Beijing　Paike　Culture　Commu.　Co.,　Ltd.　2025.