Under　the　background　of　innovation-driven　knowledge　economy　globalization,　comprehensive　and　insightful　patent　technology　information　mining　can　help　enterprises　win　the　first-mover　advantage　in　the　increasingly　fierce　technology　competition.　However,　existing　machine　learning-based　methods　do　not　entirely　incorporate　the　characteristics　of　patent　technology　of　technology　composition　and　technology　association　at　the　micro-level　and　macro-level,　making　it　difficult　to　mine　detailed　and　comprehensive　patent　information.　To　fill　this　research　gap,　firstly,　we　conduct　a　comprehensive　analysis　from　the　micro-level　technology　composition　perspective　of　patent　documents　and　the　macro-level　technology　association　perspective　of　patent　data　involved　in　the　technology　field,　and　then　we　design　a　novel　patent　ontology　that　includes　the　entity　of　patent,　function,　solution　and　application　field.　Secondly,　we　create　a　patent　heterogeneous　network　with　the　help　of　the　proposed　patent　ontology　and　the　technology　association.　Finally,　to　fully　use　the　patent　technology　characteristics,　we　develop　a　heterogeneous　graph　embedding　algorithm　to　embed　this　information　into　the　patent　representation,　and　the　experiments　done　on　non-perfluorinated　proton　exchange　membrane　patent　data　show　that　our　method　produces　better　patent　representation　than　the　comparable　models.　Furthermore,　we　utilize　the　patent　representation　to　perform　case　study　to　confirm　the　method＇s　reliability　and　practicability.　©　2022　Elsevier　Inc.