Stock　price　prediction　targets　to　predict　the　future　development　direction　of　the　stock　market　and　the　degree　of　rise　and　fall　of　the　stock　price　according　to　stock　market　quotations.　However,　since　the　stock　price　has　high　nonlinear,　high　noisy,　and　dynamic　characteristics,　it　is　challenging　to　predict　stock　prices.　This　paper　predicts　stock　price　prediction　with　a　long-short　term　memory　(LSTM)　model　according　to　the　above　data　characteristics.　First,　because　the　stock　price　is　distributed　in　different　price　ranges,　we　pre-process　the　data　by　normalizing　all　the　data　to　the　range　of　0　to　1.　Then,　we　improve　the　model　performance　by　adjusting　the　three　main　parameters,　hidden　layers,　learning　rate,　and　time　window.　LSTM　adds　a　control　part　into　LSTM　to　further　catch　these　data　to　make　the　best　of　historical　data.　We　compare　the　proposed　method　with　Recurrent　Neural　Network　(RNN)　on　a　related　dataset　with　relative　root　mean　square　error　(RRMSE),　mean　absolute　error　(MAE)　and　mean　absolute　error　percentage　(MAPE).　The　lower　the　score　on　all　three　indicators,　the　more　accurate　the　prediction.　The　experimental　results　show　that　the　scores　of　LSTM　are　lower　than　RNN　in　three　indicators,　so　its　prediction　is　more　accurate　than　RNN　with　appropriate　parameters.　Our　analyses　illustrate　that　LSTM　can　better　predict　the　dynamic　non-linear　data　like　a　stock　price　by　considering　the　historical　data.