Fundus　images　can　be　obtained　non-invasively　and　be　adopted　to　monitor　the　follow-up　on　various　fundus　diseases,　such　as　high　myopia.　Therefore,　the　use　of　fundus　images　for　the　early　screening　of　eye　diseases　has　principal　clinical　significance.　However,　AI-based　medical　research　continues　to　face　two　main　challenges,　lack　of　prior-knowledge　guidance　and　complex　fundus　information.　In　this　paper,　we　propose　a　pediatric　high　myopia　classification　model,　the　Attention-based　Patch　Residual　Shrinkage　network　(APRSnet),　which　facilitates　early　clinical　diagnosis　by　evaluating　the　importance　of　different　image　features.　A　collection　of　2492　high-resolution　fundus　images　of　children,　including　768　images　of　high　myopia　and　1724　images　of　non-high　myopia,　is　fed　to　APRSnet　as　the　training　dataset.　To　better　understand　how　different　features　impact　the　classification,　APRSnet　is　tested　on　multiple　feature-enhanced　fusion　datasets.　The　result　shows　that,　by　removing　irrelevant　information　in　fundus　images,　APRSnet　achieves　an　accuracy　of　0.959　and　an　F1　score　of　0.946　and　outperforms　all　classic　image　classification　networks　we　compared　with.　We　also　testify　that,　in　fundus　images,　gradient　information　benefits　severity　classification　the　most　since　it　helps　with　model　convergence　more　than　luminance　and　texture　information.