Unpredictable　sudden　disturbances　such　as　machine　failure,　processing　time　lag,　and　order　changes　increase　the　deviation　between　actual　production　and　the　planned　schedule,　seriously　affecting　production　efficiency.　This　phenomenon　is　particularly　severe　in　flexible　manufacturing.　In　this　paper,　a　dynamic　scheduling　method　combining　iterative　optimization　and　deep　reinforcement　learning　(DRL)　is　proposed　to　address　the　impact　of　uncertain　disturbances.　A　real-time　DRL　production　environment　model　is　established　for　the　flexible　job　scheduling　problem.　Based　on　the　DRL　model,　an　agent　training　strategy　and　an　autonomous　decision-making　method　are　proposed.　An　event-driven　and　period-driven　hybrid　dynamic　rescheduling　trigger　strategy　(HDRS)　with　four　judgment　mechanisms　has　been　developed.　The　decision-making　method　and　rescheduling　trigger　strategy　solve　the　problem　of　how　and　when　to　reschedule　for　the　dynamic　scheduling　problem.　The　data　experiment　results　show　that　the　trained　DRL　decision-making　model　can　provide　timely　feedback　on　the　adjusted　scheduling　arrangements　for　different-scale　order　problems.　The　proposed　dynamic-scheduling　decision-making　method　and　rescheduling　trigger　strategy　can　achieve　high　responsiveness,　quick　feedback,　high　quality,　and　high　stability　for　flexible　manufacturing　process　scheduling　decision　making　under　sudden　disturbance.