Hidden　Markov　model　(HMM)　is　a　statistical　model　that　is　widely　applied　in　speech　recognition,　text　information　extraction,　gait　recognition.　eye　movement　data　is　obtained　from　tracking　eye　movement　which　has　the　characters　of　temporality　and　spatiality,　and　Hidden　Markov　model　can　describe　these　two　characters.　Therefore,　Hidden　Markov　model　is　applyied　to　the　classification　Internet　user　based　on　eye　movement　data.　HMM　can　be　used　to　establish　the　formation　and　evolution　of　the　visual　behavior　related　to　different　users　online.　Different　user　models　are　established.　The　classification　principle　is　maximum　generating　probability,　the　model　of　biggest　output　probability　is　the　type　of　user.　The　genetic　algorithm　is　introduced　to　optimize　the　Initial　parameters　of　Hidden　Markov　model　to　improve　the　accuracy　of　user　classification.　The　experimental　results　show　that　the　user　classification　method　based　on　the　eye　movement　data　can　be　used　to　classify　the　old　users　and　young　users　with　high　accuracy.　It　is　proved　that　HMM　is　feasible　and　effective　for　classification.　©　Copyright　owned　by　the　author(s)　under　the　terms　of　the　Creative　Commons　Attribution-NonCommercial-NoDerivatives　4.0　International　License　(CC　BY-NC-ND　4.0).