Vulnerable　road　users　(VRUs)　involved　crashes　are　a　major　road　safety　concern　due　to　the　high　likelihood　of　fatal　and　severe　injury.　The　use　of　data-driven　methods　and　heterogeneity　models　separately　have　limitations　in　crash　data　analysis.　This　study　develops　a　hybrid　approach　of　Random　Forest　based　SHAP　algorithm　(RF-SHAP)　and　random　parameters　logit　modeling　framework　to　explore　significant　factors　and　identify　the　underlying　interaction　effects　on　injury　severity　of　VRUs-involved　crashes　in　Shenyang　(China)　from　2015　to　2017.　The　results　show　that　the　hybrid　approach　can　uncover　more　underlying　causality,　which　not　only　quantifies　the　impact　of　individual　factors　on　injury　severity,　but　also　finds　the　interaction　effects　between　the　factors　with　random　parameters　and　fixed　parameters.　Seven　factors　are　found　to　have　significant　effect　on　crash　injury　severity.　Two　factors,　including　primary　roads　and　rural　areas　produce　random　parameters.　The　interaction　effects　reveal　interesting　combination　features.　For　example,　even　though　rural　areas　and　primary　roads　increase　the　likelihood　of　fatal　crash　occurrence　individually,　the　interaction　effect　of　the　two　factors　decreases　the　likelihood　of　being　fatal.　The　findings　form　the　foundation　for　developing　safety　countermeasures　targeted　at　specific　crash　groups　for　reducing　fatalities　in　future　crashes.