The　physical　fitness　test　index　is　an　index　to　measure　the　physical　fitness　of　athletes.　It　is　used　to　evaluate　the　performance　of　athletes　in　various　events.　The　purpose　of　this　study　is　to　analyze　and　study　the　physical　fitness　test　indicators　of　athletes　based　on　data　mining　technology.　Research　methods　This　study　adopts　a　combination　of　qualitative　and　quantitative　methods.　Researchers　used　primary　and　secondary　sources　in　this　study.　Research　method　This　research　method　is　a　combination　of　qualitative　and　quantitative　methods.　This　study　uses　various　sources,　such　as　literature　review,　primary　and　secondary　data　collection,　interviews,　observations,　etc.　Build　a　fast　association　classification　model　of　physical　fitness　indicators　based　on　improved　data　mining　algorithm　to　find　out　the　association　between　physical　fitness　indicators.　The　physical　fitness　evaluation　model　can　efficiently　analyze　the　health　level　of　athletes＇　physical　fitness,　providing　a　new　research　perspective　for　physical　education　and　evaluation　of　physical　fitness　level.　This　article　takes　the　analysis　of　athletes＇　physical　fitness　test　data　as　the　research　object,　and　studies　the　principles　and　methods　of　data　mining　technology　to　solve　the　problems　in　the　management　and　analysis　process　of　athletes＇　physical　fitness　test　indicators.　This　paper　introduces　the　basic　principles,　methods,　and　processes　of　data　mining　technology,　with　emphasis　on　the　principles,　methods,　and　typical　algorithms　of　association　rule　mining　and　neural　network　mining.　An　algorithm　suitable　for　analyzing　tennis　players＇　physical　fitness　data　was　proposed　and　implemented,　and　applied　to　the　database,　discovering　unconventional　rules.　　©　2023　IEEE.