Aiming　at　the　large　memory　footprint　of　traditional　vector　machine　(Relevance　Vector　Machine,　RVM)　when　processing　big　data　in　supervised　learning,　the　idea　of　incremental　learning　is　introduced　into　the　traditional　RVM　and　the　incremental　learning　of　RVM　based　on　sparse　model　is　studied.　Method　of　an　incremental　learning　algorithm　of　RVM　based　on　sparse　model　is　proposed.　The　algorithm　considers　the　influence　of　the　existing　model　and　the　new　sample　on　the　sparse　RVM　model,　and　transforms　each　incremental　learning　to　the　problem　of　solving　the　maximized　edge　likelihood　function.　The　sparse　RVM　model　is　updated　by　solving　the　optimization　problem　continuously.　Simulation　results　show　that　this　method　can　effectively　reduce　the　memory　space　requirements.