Understanding　tourists＇　decision-making　processes,　in　which　many　factors　ranging　from　functional　attributes　to　geographical　configurations　are　highly　intertwined,　has　long　been　a　crux　for　tourism　management.　Existing　studies　are　typically　based　on　manual　surveys　that　extract　the　intricate　psychological　or　behavioural　mechanisms,　but　the　huge　expense　of　the　required　samplings　limits　the　generalization　and　comprehensiveness　of　the　findings.　This　study　proposes　a　novel　explainable　recommendation　method-Knowledge-Graph-aware　Disentangled　AutoEncoder　(KGDAE)-to　automatically　unravel　the　tourists＇　decision　processes　from　massive　historical　behaviour　data.　Based　on　the　constructed　tourism-KG　that　integrates　multidimensional　factors　into　23　types　of　entities　corresponding　to　37　semantic　and　geographic　relationships,　KGDAE　realizes　a　macro-micro　supervised　disentangled　learning　for　the　interaction　of　multiple　determinants.　Macroscopically,　the　hierarchical　attention　mechanisms　are　designed　to　distinguish　the　dominance　of　either　functional　or　geographical　factors,　and　capture　the　effect　of　the　residential　environment;　microscopically,　the　preference-propagation-based　technique　is　introduced　to　infer　the　fine-grained　characteristics　and　relations　of　tourist　interests　on　the　tourism-KG.　Extensive　experiments　show　that　KGDAE　can　effectively　restore　tourists＇　decision　processes　according　to　two　empirical　studies　while　boosting　the　recommendation　performance　compared　to　multiple　state-of-the-art　methods　with　an　increase　of　1　similar　to　19%.　Furthermore,　the　advantaged　interpretability　also　guarantees　the　robustness　of　sparse　recommendation　scenario　to　achieve　the　lowest　degradation　at　7.8%.