In　this　work　a　novel　method　was　proposed　to　predict　the　relative　solvent　accessibilities　of　residues　from　protein　primary　sequences.　This　method　was　based　on　support　vector　regression　(SVR)　and　used　the　local　information　of　the　particular　residue　for　prediction　as　input.　Three　data　sets,　RS-126,　Manesh-215　and　CB-513,　were　collected　and　used　to　evaluate　prediction　performance.　With　3-fold　cross　validation　test,　the　average　of　mean　absolute　error　(MAE)　and　correlation　coefficient　(CC)　for　different　data　set　were　consistently　better　than　a　previous　method　called　RVP-Net　which　was　based　on　a　multilayer　feed-forward　neural　network.　In　addition,　we　used　multiple　sequence　alignment　as　input　information　and　obtained　a　prediction　result　of　16.8%　for　MAE　and　0.562　for　CC,　which　was　superior　to　that　obtained　with　single　sequence　input.　The　results　demonstrate　the　efficiency　of　this　method　and　that　the　support　vector　regression　is　a　useful　tool　for　proteomics　prediction　analysis.