Fault　diagnosis　(FD)　is　crucial　for　ensuring　the　safety　and　stability　of　industrial　processes.　In　real　industrial　processes,　fault　features　in　measurement　data　are　prone　to　be　misidentified　due　to　the　feature　overlap,　which　hinders　the　effective　FD.　Besides,　the　large-scale　characteristic　of　industrial　processes　also　poses　challenges　for　FD.　To　solve　the　above　problems,　a　hierarchical　coarse-to-fine　FD　method　based　on　decision　fusion　of　class-specific　stacked　autoencoders　(DFCSSAEs)　is　proposed　to　achieve　accurate　FD　by　identifying　overlapping　features.　The　entire　FD　process　is　divided　into　three　stages:　sub-block　division,　coarse　diagnosis,　and　fine　diagnosis.　In　the　sub-block　division　stage,　for　the　large-scale　characteristic,　a　sub-block　division　method　based　on　prior　knowledge　and　mutual　information　(MI)　is　proposed　to　divide　the　whole　process　into　several　new　sub-blocks.　On　this　basis,　the　faults　with　overlapping　features　in　each　sub-block　are　identified　by　leveraging　the　feature　visualization　technique　and　summarized　into　a　new　composite　class.　In　the　coarse　diagnosis　stage,　several　SAE-Softmax-based　coarse　FD　models　are　established　to　achieve　the　targeted　diagnosis　of　individual　faults　and　the　composite　class　fault.　At　the　fine　diagnosis　stage,　a　weighted　Dempster-Shafer　(D-S)　evidence　theory　is　proposed　to　solve　decision　conflicts　among　different　coarse　FD　models　by　assigning　reasonable　weights.　Moreover,　a　new　SAE-Softmax　is　established　to　diagnose　the　unclassifiable　faults　after　the　decision　fusion　and　ultimately　improve　diagnostic　accuracy.　Finally,　the　effectiveness　and　advantages　of　our　method　are　validated　by　the　Tennessee　Eastman　process　(TEP)　and　the　real　dataset　of　the　PRONTO　process.　Experimental　results　demonstrate　that　our　method　achieves　high　FD　rates　of　0.962　and　0.997.