In　modern　multi-core　computer　architectures,　composite　prefetching　techniques　exhibit　promising　potential　in　efficiently　handling　diverse　memory　access　patterns.　However,　the　competition　among　multiple　prefetchers　for　limited　shared　resources　poses　challenges　to　both　system　performance　and　fairness.　To　enhance　the　efficiency　and　fairness　of　multi-core　systems,　we　propose　FAPM:　a　fairness-aware　prefetching　mechanism　based　on　reinforcement　learning　(RL)　for　multi-core　systems.　FAPM　adopts　a　RL　strategy　to　dynamically　adjust　the　prefetcher＇s　activation　and　prefetch　degree　during　runtime,　enabling　dynamic　management　of　multiple　prefetchers　in　the　multi-core　system.　In　the　design　of　the　reward　function,　we　comprehensively　consider　the　accuracy　and　timeliness　of　prefetches　on　individual　cores,　as　well　as　the　fairness　in　multi-core　systems.　Additionally,　we　propose　a　new　runtime　method　to　calculate　a　proxy　metric　for　fair-speedup　to　evaluate　the　fairness　of　the　system.　Through　this　comprehensive　reward　mechanism,　FAPM　effectively　optimizes　the　performance　of　individual　cores　and　enhances　the　fairness　of　the　system.　Extensive　experiments　on　multi-core　systems　demonstrate　significant　improvements.　FAPM　achieves　up　to　a　17.64%　improvement　in　fair-speedup,　which　indicates　a　significant　enhancement　in　system　fairness.　This　approach　provides　a　practical　and　efficient　solution　to　the　resource　contention　challenge,　while　also　holding　the　potential　to　improve　fairness　and　performance　in　large-scale　computing　systems.　©　2023　IEEE.