Group　activity　recognition　has　received　significant　interest　due　to　its　widely　practical　applications　in　sports　analysis,　intelligent　surveillance　and　abnormal　behavior　detection.　In　a　complex　multi-person　scenario,　only　a　few　key　actors　participate　in　the　overall　group　activity　and　others　may　bring　irrelevant　information　for　recognition.　However,　most　previous　approaches　model　all　the　actors＇　actions　in　the　scene　equivalently.　To　this　end,　we　propose　a　relation-guided　actor　attention　(RGAA)　module　to　learn　reinforced　feature　representations　for　effective　group　activity　recognition.　First,　a　location-aware　relation　module　(LARM)　is　designed　to　explore　the　relation　among　pairwise　actors＇　features　in　which　appearance　and　position　information　are　both　considered.　We　propose　to　stack　all　the　pairwise　relation　features　and　the　features　themselves　of　an　actor　to　learn　actor　attention　which　determines　the　importance　degree　from　local　and　global　information.　Extensive　experiments　on　two　publicly　benchmarks　demonstrate　the　effectiveness　of　our　method　and　the　state-of-the-art　performance　is　achieved.