In　manufacturing　systems,　implementing　preventive　maintenance　(PM)　is　essential　for　ensuring　sustainable　production　since　the　inevitable　wear　and　tear　of　machines　can　significantly　affect　production　efficiency.　In　today＇s　decentralized　economy,　distributed　shop　scheduling　has　emerged　within　the　framework　of　distributed　manufacturing　to　reduce　costs,　enhance　efficiency,　and　strengthen　competitiveness.　Thus,　this　paper　proposes　a　learning-based　memetic　algorithm　(LMA)　for　addressing　the　energy-efficient　distributed　flow-shop　scheduling　problem　with　preventive　maintenance　(EDFSP-PM)　to　minimize　both　makespan　and　total　energy　consumption　simultaneously.　First,　a　mathematical　model　is　formulated　and　encoding　and　decoding　methods　are　developed　to　map　solutions　to　schedules　with　consideration　of　PM　operations.　Second,　two　heuristics　are　employed　to　generate　high-quality　solutions　and　various　problem-specific　operators　are　designed　for　different　sub-problems　and　objectives.　Third,　a　hierarchical　learning　mechanism　is　proposed　via　employing　multi-layer　Q-learning　to　select　appropriate　operators　for　solutions　with　diverse　characteristics.　Fourth,　a　feedback　learning　mechanism　with　solution　pool　is　devised　to　reintegrate　solutions　from　the　pool　into　the　search　process　to　enhance　search　efficiency.　Finally,　numerical　experiments　are　conducted　to　verify　the　effectiveness　of　the　designed　mechanisms.　The　comparative　results　demonstrate　superior　performance　of　the　proposed　LMA　in　terms　of　convergence　and　diversity.