The　safety　of　hydrogen　refueling　stations　(HRSs)　is　receiving　increasing　attention　with　the　growth　use　of　hydrogen　energy.　Existing　risk　assessment　methods　of　HRS　are　primarily　based　on　expert　knowledge,　which　is　affected　by　potential　subjectivity.　This　paper　aims　to　present　a　new　hybrid　risk　assessment　method　incorporating　HRS　accident　data　and　physical　knowledge　into　a　Bayesian　network　(BN)　model　to　analyze　the　key　risk　influencing　factors　(RIFs).　The　HRS　accident　data　in　HIAD　2.1　from　1980　to　2023　is　used　in　this　paper,　and　30　RIFs　are　identified　based　on　the　accident　report　information　and　physical　knowledge.　To　address　the　issue　of　the　insufficient　accident　data　for　BN　modeling,　the　accident　data　is　expanded　by　Conditional　Tabular　Generative　Adversarial　Networks　(CTGAN).　Bayesian　search,　Peter-Clark　algorithm　and　Greedy　Thick　Thinning　methods　are　adopted　for　structure　learning.　The　expectation　maximization　algorithm　is　employed　for　parameter　learning　in　the　BN　model.　Additionally,　K-fold　cross　validation　is　used　when　testing　the　performance　of　different　BN　models.