Recently,　vulnerable　road　user　to　motor　vehicle　(VRU-MV)　crashes　have　been　the　focus　of　much　attention.　Data　from　Shenyang,　China　were　used　for　crashes　analysis　and　significant　differences　existed　in　VRU-MV　crashes　across　seasons.　Therefore,　this　study　analysed　three　crash　datasets　(whole,　＂spring　&　summer＂,　＂fall　&　winter＂)　by　using　a　hybrid　method　integrating　random　parameter　logit　(RP-logit)　model　and　Bayesian　network　(BN).　First,　RP-logit　model　was　established　to　find　out　significant　factors　and　heterogeneity　considering　those　three　datasets.　Second,　significant　factors　identified　from　RP-logit　model　were　utilized　to　establish　a　BN　to　investigate　statistical　associations　between　injury　severity　and　explanatory　attributes.　Results　showed　seven　significant　factors　were　found　in　＂spring　&　summer＂　dataset　while　five　significant　factors　in　＂fall　&　winter＂　dataset,　four　hidden　factors　were　found　by　comparative　analysis　of　those　three　datasets.　Besides,　five　factors　were　found　to　be　random　and　normally　distributed.　Results　of　BN　indicated　that　some　factors　could　significantly　increase　high　possibility　of　fatality　when　combined　with　other　factors.　For　example,　functional　zone　(ZON)　in　＂spring　&　summer＂　dataset　and　motor　vehicle　type　(MVT)　in　＂fall　&　winter＂　dataset.　The　proposed　hybrid　method　demonstrated　both　the　consistence　of　methods　(RP-logit　model　&　BN)　and　the　differences　across　seasons　for　VRU-MV　crashes　analysis.　Three　personalized　factors　including　physical　isolation　(PI),　crash　type　(CT)　and　motor　vehicle　type　(MVT)　made　great　differences　across　seasons.