Current　electric　vehicle　charging　navigation　(EVCN)　models　typically　treat　the　problem　as　a　static,　one-shot　decision,　neglecting　the　user　regret　psychology　and　myriad　uncertainties　that　unfold　throughout　the　charging　journey.　Moreover,　by　restricting　decision-making　to　a　predefined　roster　of　conventional　charging　strategies　and　simply　selecting　the　option　with　the　lowest　retrospective　regret,　these　methods　risk　overlooking　novel　or　dynamic　policies　that　could　yield　even　greater　satisfaction.　To　address　these　problems,　a　user　regret　psychology-driven　EVCN　strategy　based　on　deep　reinforcement　learning　(DRL)　and　transfer　learning　(TL)　is　proposed.　In　the　EVCN　problem,　multiple　uncertainties　are　considered,　and　several　commonly　used　charging　strategies　are　selected　as　comparison　strategies　to　construct　a　regret-theory-based　EVCN　model.　Subsequently,　to　accurately　estimate　the　attributes　of　comparison　strategies　under　uncertain　conditions,　a　TL-based　attribute　estimation　method　is　developed,　utilizing　the　replay　buffer　and　an　improved　Jaccard　similarity.　Based　on　this,　a　DRL　algorithm　is　used　for　fast　and　efficient　model　optimization.　Finally,　the　proposed　method　is　simulated　on　the　transportation　network,　and　the　results　demonstrate　that　the　proposed　method　improves　the　driving　time　estimation　accuracy　of　comparison　strategies,　significantly　lowers　the　charging　strategy　regret,　and　exhibits　excellent　adaptability　and　scalability.