Background　Feature　matching　technology　is　vital　to　establish　the　association　between　virtual　and　real　objects　in　virtual　reality　and　augmented　reality　systems.Specifically,it　provides　them　with　the　ability　to　match　a　dynamic　scene.Many　image　matching　methods,of　which　most　are　deep　learning-based,have　been　proposed　over　the　past　few　decades.However,vessel　fracture,stenosis,artifacts,high　background　noise,and　uneven　vessel　gray-scale　make　vessel　matching　in　coronary　angiography　extremely　difficult.Traditional　matching　methods　perform　poorly　in　this　regard.Methods　In　this　study,a　topological　distance-constrained　feature　descriptor　learning　model　is　proposed.This　model　regards　the　topology　of　the　vasculature　as　the　connection　relationship　of　the　centerline.The　topological　distance　combines　the　geodesic　distance　between　the　input　patches　and　constrains　the　descriptor　network　by　maximizing　the　feature　difference　between　connected　and　unconnected　patches　to　obtain　more　useful　potential　feature　relationships.Results　Matching　patches　of　different　sequences　of　angiographic　images　are　generated　for　the　experiments.The　matching　accuracy　and　stability　of　the　proposed　method　is　superior　to　those　of　the　existing　models.Conclusions　The　proposed　method　solves　the　problem　of　matching　coronary　angiographies　by　generating　a　topological　distance-constrained　feature　descriptor.