In　this　paper,　we　investigate　a　typical　clustering　technology,　namely,　Gaussian　mixture　model　(GMM)-based　approach,　for　user　interest　prediction　in　social　networks.　The　establishment　of　the　model　follows　the　following　process:　collect　dataset　from　4613　users　and　more　than　16　million　messages　from　Sina　Weibo;　obtain　each　user＇s　interest　eigenvalue　sequence　and　establish　GMM　model　to　clustering　users.　In　theory　and　experiment,　this　approach　is　feasible.　The　GMM-based　approach　considers　the　prediction　accuracy　and　consuming　time.　A　series　of　experiments　are　conducted　to　validate　the　feasibility　and　efficiency　of　the　proposed　solution　and　whether　it　can　achieve　a　higher　accuracy　of　prediction　compared　with　other　approaches,　such　as　SVM　and　K-means.　Further　experiments　show　that　GMM-based　approach　could　produce　higher　prediction　accuracy　of　93.9%,　thus　leveraging　computation　complexity.