Data　association　is　the　premise　and　basis　of　state　estimation　of　mobile　robot　simultaneous　localization　and　mapping　(SLAM).　In　order　to　solve　the　problem　of　complex　and　time-consuming　computation　of　joint　compatiblity　branch　and　bound　algorithm,　a　SLAM　data　association　algorithm　based　on　Gaussian　mixture　model　(GMM)　maximum　expectation　(EM)　clustering　is　proposed.　Firstly,　in　order　to　reduce　the　number　of　observations　participating　in　the　association　at　the　same　time,　group　the　current　measurement　using　maximum　expectation　clustering　algorithm　for　gaussian　mixture　model　in　the　local　region.　Secondly,　conduct　data　association　using　joint　compatibility　branch　and　bound　algorithm　for　each　group.　Finally,　obtain　the　optimal　correlation　result　by　combining　the　correlation　result　between　each　observation　group　and　the　local　map　features.The　simulation　results　show　that　the　SLAM　data　association　algorithm　based　on　gaussian　mixture　model　maximum　expectation　clustering　guarantees　the　accuracy　of　data　association,　the　computational　complexity　of　this　method　is　reduced　and　the　running　time　is　shortened.　©　2020,　Editorial　Department　of　Control　Theory　&　Applications.　All　right　reserved.