The　development　of　New　Energy　Vehicles　(NEVs)　has　contributed　to　the　alleviation　of　environmental　pollution　in　the　transportation　sector.　Although　NEVs　have　some　advantages,　such　as　energy　saving,　being　environmentally　friendly,　and　low　noise,　they　are　restricted　by　their　cruising　range　or　recharging-related　problems.　To　minimize　such　disadvantages,　an　optimal　plan　for　the　charging　station　site　is　necessary.　This　paper　proposes　a　Large-Scale　Group　Decision-Making　(LSGDM)　method　to　select　the　best　location　for　such　charging　stations.　This　method　involves　a　large　group　of　experts　providing　their　preferences　according　to　their　knowledge　and　background.　To　facilitate　the　elicitation,　our　method　uses　Hesitant　Fuzzy　Linguistic　Term　Sets　(HFLTS)　and　Extended　Comparative　Linguistic　Expressions　with　Symbolic　Translation　(ELICIT)　model　to　improve　the　elicitation　process,　guarantee　precise　computing　processes　and　obtain　interpretable　results.　A　social　network　is　then　constructed　based　on　experts＇　preference　similarity　and　trust　relationships,　which　reflects　both　their　relationship　and　its　strength　simultaneously.　Afterwards,　a　social　analysis-based　clustering　process　groups　the　experts,　and　a　Three-Way　Decision　(TWD)-based　Consensus　Reaching　Process　(CRP)　is　introduced　to　improve　the　group＇s　agreement.　Finally,　a　selection　of　charging　station　site　case　studies　is　conducted,　and　a　comparative　analysis　is　carried　out　to　illustrate　the　quality　of　the　proposal.