In　view　of　the　current　multi-source　information　scenario,　this　paper　proposes　a　decision-making　method　for　electric　vehicle　charging　stations　(EVCSs)　based　on　prospect　theory,　which　considers　payment　cost,　time　cost,　and　route　factors,　and　is　used　for　electric　vehicle　(EV)　owners　to　make　decisions　when　the　vehicle’s　electricity　is　low.　Combined　with　the　multi-source　information　architecture　composed　of　an　information　layer,　algorithm　layer,　and　model　layer,　the　load　of　EVCSs　in　the　region　is　forecast.　In　this　paper,　the　Monte　Carlo　method　is　used　to　test　the　IEEE-30　model　and　the　traffic　network　based　on　it,　and　the　spatial　and　temporal　distribution　of　charging　load　in　the　region　is　obtained,　which　verifies　the　effectiveness　of　the　proposed　method.　The　results　show　that　EVCS　load　forecasting　based　on　the　prospect　theory　under　the　influence　of　multi-source　information　will　have　an　impact　on　the　space–time　distribution　of　the　EVCS　load,　which　is　more　consistent　with　the　decisions　of　EV　owners　in　reality.　©　2022　by　the　authors.