Crowdfunding　has　become　important　in　increasing　financial　support　for　the　development　of　green　technologies.　Self-disclosed　information　significantly　affects　supporters＇　decisions　and　is　important　for　the　success　of　green　project　funding.　However,　current　studies　still　lack　investigations　into　the　impact　of　information　disclosure　on　green　crowdfunding　performance.　This　research　aims　to　fill　this　knowledge　gap　by　exploring　eight　information　disclosure-relevant　factors　in　green　crowdfunding　performance.　Applying　machine　learning　techniques　(e.g.,　Natural　Language　Processing　and　Computer　Vision)　and　logistic　regression,　this　study　investigates　720　green　crowdfunding　campaigns　on　GoFundMe　and　empirically　finds　that　the　duration,　length　of　campaign　introductions,　and　length　of　the　title　influence　fundraising　outcomes.　However,　no　evidence　supports　the　impact　of　goal　size,　emotion　of　campaign　introduction,　or　image　content　on　funding　success.　This　study　clarifies　the　information　disclosure-related　data　that　green　crowdfunding　campaigns　should　consider　and　provides　founders　with　a　constructive　guide　to　smoothly　raise　money　for　a　green　crowdfunding　campaign.　This　study　also　contributes　to　data　processing　methods　by　providing　future　studies　with　an　approach　for　transferring　unstructured　data　to　structured　data.