In　this　paper,　we　propose　a　modified　proximal　gradient　method　for　solving　a　class　of　nonsmooth　convex　optimization　problems,　which　arise　in　many　contemporary　statistical　and　signal　processing　applications.　The　proposed　method　adopts　a　new　scheme　to　construct　the　descent　direction　based　on　the　proximal　gradient　method.　It　is　proven　that　the　modified　proximal　gradient　method　is　Q-linearly　convergent　without　the　assumption　of　the　strong　convexity　of　the　objective　function.　Some　numerical　experiments　have　been　conducted　to　evaluate　the　proposed　method　eventually.　©　2017,　Operations　Research　Society　of　China,　Periodicals　Agency　of　Shanghai　University,　Science　Press,　and　Springer-Verlag　Berlin　Heidelberg.