The　surface　electromyogram　(sEMG)　contains　a　wealth　of　motion　information,　which　can　reflect　user＇s　muscle　motion　intentions.　The　decoding　based　on　sEMG　has　been　widely　used　to　provide　a　safe　and　effective　human-computer　interaction　(HCI)　method　for　neural　prosthesis　and　exoskeleton　robot　control.　The　motor　intention　decoding　based　on　low　sampling　frequency　sEMG　may　promote　the　application　of　wearable　low-cost　EMG　sensors　in　HCI.　Therefore,　a　motor　intention　decoding　scheme　suitable　for　low　frequency　EMG　signal　is　proposed　in　this　paper,　that　is,　transfer　learning　based　on　Alexnet.　Moreover,　the　effects　of　different　feature　extraction　methods　and　data　augmentation　with　Gaussian　white　noise　are　fully　analyzed.　The　proposed　algorithm　is　evaluated　with　the　NinaPro　database　5.　The　highest　accuracy　can　reach　70.4%+/-　4.36%　in　53　gestures　identification　of　10　subjects.　Some　classical　machine　learning　algorithms　such　as　support　vector　machine　(SVM),　linear　discriminant　analysis　(LDA)　and　K　Nearest　Neighbor　(KNN)　are　chosen　to　make　comparison,　where　the　SVM　with　Gaussian　kernel　function　reaches　to　the　maximum　accuracy　of　67.98%+/-　4.56%.　Two-way　variance　results　show　significant　differences　between　each　other.　The　experiment　results　show　that　the　transfer　learning　is　effective　for　decoding　low-frequency　sEMG　for　a　large　number　of　gestures.