High　myopia　is　one　of　the　leading　causes　of　fundus　diseases.　If　it　can　be　found　and　treated　in　time,　the　risk　of　fundus　lesions　in　children＇s　growth　will　be　reduced,　and　the　growth　rate　of　patients　with　visual　disabilities　will　be　effectively　controlled.　Vascular,　as　one　of　the　significant　features　in　fundus　images,　are　often　used　as　additional　elements　to　help　ophthalmologists　diagnose.　Hence,　in　this　paper,　based　on　the　idea　of　homogeneous　multimodality,　we　design　a　neural　network　model　with　two　branches　that　simultaneously　processing　the　vascular　feature　image　and　original　fundus　image　therefore　to　automatic　detect　high　myopia　based　on　the　fundus　images,　and　hope　to　make　a　great　difference　in　clinical　practice.　Extensive　comparative　experiments　were　conducted　between　our　method　and　other　general　classification　models　through　a　private　retinal　fundus　data　set.　The　results　show　that　our　method　achieves　the　best　performance　of　93.4%　in　accuracy.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.