Currently,　tourism　management　research　is　focused　on　comprehending　the　fluctuating　tourist　preferences　and　devising　targeted　development　strategies　through　extensive　analysis　of　heterogenous　user-generated　contents.　However,　given　the　online　reviews　of　attractions　involve　overabundant　mixed　and　intangible　dimensions,　the　widely-used　unsupervised　text　mining　could　be　incomplete　or　inaccurate.　Furthermore,　the　existing　literature　typically　restricted　to　the　certain　types　of　attractions　within　several　tourist　destinations　and　origins,　can　hardly　guarantee　comprehensive　insights.　To　overcome　these　limitations,　the　study　proposes　a　novel　knowledgegraph-driven　framework,　involving　the　systematic　construction　as　well　as　the　thorough　investigation　and　inference　of　a　tourism-oriented　knowledge　graph　(TKG).　Following　the　ontology　of　domain　expertise,　11,296,716　structured　triplets　of　multifaceted　knowledge　about　1,174,034　tourists　and　20,481　attractions　within　all　340　city-level　destinations　across　China　are　extracted　from　multi-source　text　corpus　by　the　transferring　learning　on　pre-training　language　model　with　43.64-50.65　%　accuracy　enhancement.　In　virtue　of　TKG,　a　comprehensive　decision-support　system　can　be　established,　which　bifurcates　into　two　distinct　modes　of　knowledge　application:　symbolic　query　and　distributed　reasoning.　Through　the　implementation　of　multiple　spatiotemporal　analyses　via　SPARQL　queries　on　TKG,　the　distribution　regularities　of　tourist　preference,　causal　interpretations,　and　their　effects　on　destination　development　can　be　progressively　detected.　Refining　the　distributed　representations　of　objects　by　injecting　abundant　contextual　knowledge　from　TKG　can　significantly　enhance　the　downstream　inferential　tasks,　such　as　tourist　demand　prediction　and　attraction　competitive　intelligence.