The　rapid　development　of　nanogenerator　technology　is　seen　as　potentially　having　a　major　impact　on　people＇s　lives　and　would　transform　the　existing　energy　industry.　Monitoring　the　emergence　of　nanogenerator　technology　is　essential　to　understanding　and　detecting　its　changing　trends　at　early　stages.　This　information　is　crucial　for　academic　and　government　research,　the　development　of　strategic　planning,　social　investment　and　enterprise　practices.　Therefore,　this　paper　proposes　a　framework　that　uses　academic　papers　and　patents　as　data　resources　and　integrates　citation　analysis　and　text　mining　to　monitor　the　evolutionary　path　of　nanogenerator　technology　and　forecast　its　trends.　We　began　by　using　citation　analysis　to　mine　the　technical　knowledge　contained　in　academic　papers　and　to　monitor　the　evolutionary　path　of　nanogenerator　technology.　This　was　followed　by　employing　the　applied　Hierarchical　Dirichlet　Process　(HDP)　topic　model　which　is　a　kind　of　text　mining　method　used　to　extract　the　technical　topics　contained　in　academic　papers.　We　then　analyzed　the　differences　from　the　results　of　the　HDP　topic　model　and　citation　analysis　to　improve　the　evolutionary　path　of　nanogenerator　technology　missing　details　due　to　the　time　lag　of　citation　analysis.　Further　application　of　the　HDP　topic　model　allowed　us　to　extract　the　technical　topics　contained　in　patents.　Finally,　we　carried　out　the　analysis　of　gaps　between　science　and　technology,　combining　them　with　expert　knowledge　and　the　evolutionary　path　of　nanogenerator　technology　to　forecast　development　trends.　This　paper　contributes　to　our　understanding　of　how　nanogenerator　technology　emerges　and　develops　and　will　be　of　specific　interest　to　nanogenerator　technology　R&D　experts.