This　paper　presents　a　dynamic　stochastic　decision-makingmethod　that　considers　the　characteristics　of　stochastic　variable　variances　under　time-sequence　contingency　environments　for　solving　stochastic　decision-making　problems　with　information　from　different　periods　and　of　indeterminate　attribute　weights.　First,　time-sequence　weights　are　obtained　using　the　technique　for　order　preference　by　similarity　to　ideal　solution　(TOPSIS),　corresponding　with　the　idea　of　＂stressing　the　present　rather　than　the　past.＂　After　determining　the　time　degree　and　fully　considering　the　characteristics　of　normally　distributed　stochastic　variable　variances,　the　attribute　weight　is　determined　based　on　vertical　projection　distance.　Decision-making　information　is　then　assembled　from　two　dimensions　of　time-sequence　and　attributes,　based　on　the　two　categories　of　weighted　arithmetic　averaging　operators　of　normally　distributed　stochastic　variables,　resulting　in　comprehensive　dynamic　decision-making　from　single　solution　dimensions　and　a　priority　sequence　of　solutions　per　the　order　relation　criteria　of　normally　distributed　stochastic　variables.　Finally,　the　validity　and　practicability　of　the　methods　proposed　in　this　paper　are　verified　using　an　example　numerical　analysis.