As　the　uneasy　sense　of　three-dimensional　space　and　non-intuitiveness　of　resources　information,　it　is　difficult　for　users　to　find　interesting　or　valuable　information　in　network　virtual　environments.　Therefore,　a　new　personalized　information　recommendation　algorithm　based　on　MPF　users　clustering　would　be　proposed　in　this　paper.　The　MPF,　namely　Fuzzy　C-Means　(FCM)　clustering　based　on　Multi-Objects　Particle　Swarm　Optimization　(MOPSO),　combines　the　respective　advantages　of　PSO　and　FCM,　which　could　prevent　from　those　defects,　for　FCM　to　be　susceptible　to　initial　value　and　noisy　data,　and　PSO　to　be　easily　falling　into　local　optimum　and　so　on.　In　order　to　improve　clustering　effect,　we　design　the　particle　fitness　function　based　on　dual-objectives　(intra-class　distance　and　inter-class　distance)　in　PSO.　Finally,　the　standard　data　set　and　simulation　data　set　are　applied　to　test　the　personalized　information　recommendation　algorithm　based　on　MPF　users　clustering.　The　experimental　result　shows　that　this　algorithm　was　of　good　performances.　©　2011　IEEE.