Targeting　the　challenges　in　the　risk　analysis　of　laboratory　fire　accidents,　particularly　considering　fire　accidents　in　Chinese　universities,　an　integrated　approach　is　proposed　with　the　combination　of　association　rule　learning,　a　Bayesian　network　(BN),　and　fuzzy　set　theory　in　this　study.　The　proposed　approach　has　the　main　advantages　of　deriving　conditional　probabilities　of　BN　nodes　based　on　historical　accident　data　and　association　rules　(ARs)　and　making　good　use　of　expert　elicitation　by　using　an　augmented　fuzzy　set　method.　In　the　proposed　approach,　prior　probabilities　of　the　cause　nodes　are　determined　based　on　expert　elicitation　with　the　help　of　an　augmented　fuzzy　set　method.　The　augmented　fuzzy　set　method　enables　the　effective　aggregation　of　expert　opinions　and　helps　to　reduce　subjective　bias　in　expert　elicitations.　Additionally,　an　AR　algorithm　is　applied　to　determine　the　probabilistic　dependency　between　the　BN　nodes　based　on　the　historical　accident　data　of　Chinese　universities　and　further　derive　conditional　probability　tables.　Finally,　the　developed　fuzzy　Bayesian　network　(FBN)　model　was　employed　to　identify　critical　causal　factors　with　respect　to　laboratory　fire　accidents　in　Chinese　universities.　The　obtained　results　show　that　H4　(bad　safety　awareness),　O1　(improper　storage　of　hazardous　chemicals),　E1　(environment　with　hazardous　materials),　and　M4　(inadequate　safety　checks)　are　the　four　most　critical　factors　inducing　laboratory　fire　accidents.