Atmospheric　visibility　is　an　indicator　of　atmospheric　transparency　and　its　range　directly　reflects　the　quality　of　the　atmospheric　environment.　With　the　acceleration　of　industrialization　and　urbanization,　the　natural　environment　has　suffered　some　damages.　In　recent　decades,　the　level　of　atmospheric　visibility　shows　an　overall　downward　trend.　A　decrease　in　atmospheric　visibility　will　lead　to　a　higher　frequency　of　haze,　which　will　seriously　affect　people＇s　normal　life,　and　also　have　a　significant　negative　economic　impact.　The　causal　relationship　mining　of　atmospheric　visibility　can　reveal　the　potential　relation　between　visibility　and　other　influencing　factors,　which　is　very　important　in　environmental　management,　air　pollution　control　and　haze　control.　However,　causality　mining　based　on　statistical　methods　and　traditional　machine　learning　techniques　usually　achieve　qualitative　results　that　are　hard　to　measure　the　degree　of　causality　accurately.　This　article　proposed　the　seq2seq-LSTM　Granger　causality　analysis　method　for　mining　the　causality　relationship　between　atmospheric　visibility　and　its　influencing　factors.　In　the　experimental　part,　by　comparing　with　methods　such　as　linear　regression,　random　forest,　gradient　boosting　decision　tree,　light　gradient　boosting　machine,　and　extreme　gradient　boosting,　it　turns　out　that　the　visibility　prediction　accuracy　based　on　the　seq2seq-LSTM　model　is　about　10%　higher　than　traditional　machine　learning　methods.　Therefore,　the　causal　relationship　mining　based　on　this　method　can　deeply　reveal　the　implicit　relationship　between　them　and　provide　theoretical　support　for　air　pollution　control.