Benign　Paroxysmal　Positional　Vertigo　(BPPV)　is　a　prevalent　vestibular　disorder　encountered　in　clinical　settings.　Diagnosis　of　this　condition　primarily　relies　on　the　observation　of　nystagmus,　which　involves　monitoring　the　eye　movements　of　patients.　However,　existing　medical　equipment　for　collecting　and　analyzing　nystagmus　data　has　notable　limitations　and　deficiencies.　To　address　this　challenge,　a　comprehensive　BPPV　nystagmus　data　collection　and　intelligent　analysis　framework　has　been　developed.　Our　framework　leverages　a　neural　network　model,　Egeunet,　in　conjunction　with　mathematical　statistical　techniques　like　Fast　Fourier　Transform　(FFT),　enabling　precise　segmentation　of　eye　structures　and　accurate　analysis　of　eye　movement　data.　Furthermore,　an　eye　movement　analysis　method　has　been　introduced,　designed　to　enhance　clinical　decision-making,　resulting　in　more　intuitive　and　clear　analysis　outcomes.　Benefiting　from　the　high　sensitivity　of　our　eye　movement　capture　and　its　robustness　in　the　face　of　environmental　conditions　and　noise,　our　BPPV　nystagmus　data　collection　and　intelligent　analysis　framework　has　demonstrated　outstanding　performance　in　BPPV　detection.