Non-invasive　fundus　images　can　be　used　to　diagnose　various　fundus　diseases,　such　as　high　myopia　(HM).　Existing　deep　learning-based　research　mainly　relies　on　data　to　drive　the　model　to　learn　key　features.　However,　the　data　related　to　HM　is　limited　(especially　for　young　children),　making　it　difficult　for　deep　networks　to　accurately　focus　on　key　features.　Hence,　we　propose　a　prior　knowledge-guided　deep　learning　network　for　pediatric　HM　detection.　It　comprises　four　modules:　(1)　Prior　Feature-Based　Channel　Fusion:　This　module　extracts　key　features　(brightness,　edges,　texture)　from　fundus　images　using　image　processing　methods　to　obtain　corresponding　single-channel　slices.　Through　channel-level　feature　fusion,　these　slices　are　used　to　construct　multiple　sets　of　feature-enhanced　datasets.　(2)　Global　Fundus　Feature　Extraction:　It　uses　residual　blocks　to　build　the　backbone,　and　builds　a　U-shaped　attention　component　based　on　the　U-shaped　network.　This　module　extracts　the　global　and　context　information　of　the　original　fundus　image　to　obtain　a　global　feature　map.　(3)　Knowledge-Guided　Attention　Generation:　The　residual　structure　is　employed　to　further　extract　the　hidden　features　of　the　feature-enhanced　data,　thereby　obtaining　local　key　feature　maps.　(4)　Pediatric　HM　Classification:　By　combining　local　key　feature　maps　(obtained　in　module　3)　with　global　feature　maps　(obtained　in　module　2)　through　spatial　attention　mechanism,　the　deep　network　is　guided　to　complete　the　classification　task　of　pediatric　HM.　Extensive　experiments　on　real-world　datasets　demonstrate　the　effectiveness　of　our　method　(accuracy　is　0.921,　F1　score　is　0.903).