This　paper　addresses　the　path　prediction　problem　of　multiple　pedestrians　interacting　in　dynamic　scene,　which　is　a　great　challenge　because　of　the　complexity　and　subjectivity　of　pedestrian　movements.　To　forecast　the　future　trajectory　of　a　pedestrian,　it　is　necessary　to　consider　pedestrian　subjective　intention　and　social　interaction　information.　Previous　methods　ignore　that　important　position　nodes　of　historical　trajectories　can　reflect　the　subjective　intention　of　pedestrians　in　complex　trajectories.　In　this　work,　we　present　Person-Social　Twin-Attention　network　based　on　Gated　Recurrent　Unit　(PSA-GRU),　which　can　fully　utilize　important　position　nodes　of　personal　historical　trajectory　and　social　interaction　information　between　pedestrians.　In　our　approach,　the　person-attention　encoding　module　extracts　the　most　salient　parts　of　personal　history　trajectory　and　helps　model　learn　where　and　how　pedestrians　will　go.　Meanwhile,　the　social-attention　pool　module　contributes　to　keep　distance　among　different　pedestrians　and　simulates　the　dynamic　interaction　of　all　pedestrians　in　real　scenes.　PSA-GRU　also　takes　advantage　of　Gated　Recurrent　Unit　(GRU)　to　enable　the　model　to　improve　the　computational　efficiency.　Experimental　results　demonstrate　that　the　prediction　accuracy　and　efficiency　of　our　proposed　model　are　both　greatly　improved　on　UCY　and　ETH　datasets.　©　2021　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.