Glaucoma　is　a　chronic　and　irreversible　eye　disease.　Early　detection　and　treatment　can　effectively　prevent　severe　consequences.　Deep　transfer　learning　is　widely　used　in　fundus　imaging　analysis　to　remedy　the　shortage　of　training　data　of　glaucoma.　The　model　trained　on　the　source　domain　may　struggle　to　predict　glaucoma　in　the　target　domain　due　to　distribution　differences.　Several　limitations　cannot　be　ignored:　(1)　Image　matching:　enhancing　global　and　local　image　consistency　through　bidirectional　matching;　(2)　Hierarchical　transfer:　developing　a　strategy　for　transferring　different　hierarchical　features.　To　this　end,　we　propose　a　novel　Matched　Hierarchical　Transfer　Network　(MHT-Net)　to　achieve　automatic　glaucoma　detection.　We　initially　create　a　fundus　structure　detector　to　match　global　and　local　images　using　intermediate　layers　of　a　pre-trained　diagnostic　model　with　source　domain　data.　Next,　a　hierarchical　transfer　network　is　implemented,　sharing　parameters　for　general　features　and　using　a　domain　discriminator　for　specific　features.　By　integrating　adversarial　and　classification　losses,　the　model　acquires　domain-invariant　features,　facilitating　precise　and　seamless　transfer　of　fundus　information　from　source　to　target　domains.　Extensive　experiments　demonstrate　the　effectiveness　of　our　proposed　method,　outperforming　existing　glaucoma　detection　methods.　These　advantages　endow　our　algorithm　as　a　promising　efficient　assisted　tool　in　the　glaucoma　screening.　©2015　IEEE.