Causal　relationship　mining　of　multi-dimensional　meteorological　time　series　data　can　reveal　the　potential　connection　between　visibility　and　other　influencing　factors,　and　it　is　meaningful　in　terms　of　environmental　management,　air　pollution　chasing,　and　haze　control.　However,　because　causality　analysis　based　on　statistical　methods　or　traditional　machine　learning　techniques　cannot　represent　the　complex　relationship　between　visibility　and　its　influencing　factors,　as　an　extension　of　traditional　Granger-causality　analysis,　we　propose　a　causality　mining　method　based　on　the　seq2seq-LSTM　deep　learning　model.　The　method　can　profoundly　reveal　the　hidden　relationship　between　different　features　and　visibility　and　the　regular　pattern　of　bad　weather,　which　can　provide　theoretical　support　for　air　pollution　control.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.