长短期记忆人工神经网络（Long-Short Term Memory, LSTM）是一种时间递归神经网络(RNN)，论文首次发表于1997年。由于独特的设计结构，LSTM适合于处理和预测时间串行中间隔和延迟非常长的重要事件。

LSTM的表现通常比时间递归神经网络及隐马尔科夫模型（HMM）更好，比如用在不分段连续手写识别上。2009年，用LSTM构建的人工神经网络模型赢得过ICDAR手写识别比赛冠军。LSTM还普遍用于自主语音识别，2013年运用TIMIT自然演讲数据库达成17.7%错误率的纪录。作为非线性模型，LSTM可作为复杂的非线性单元用于构造更大型深度神经网络。

Long short-term memory (LSTM) is a recurrent neural network (RNN) architecture (an artificial neural network) published in 1997 by Sepp Hochreiter and Jürgen Schmidhuber. Like most RNNs, an LSTM network is universal in the sense that given enough network units it can compute anything a conventional computer can compute, provided it has the proper weight matrix, which may be viewed as its program. Unlike traditional RNNs, an LSTM network is well-suited to learn from experience to classify, process and predict time series when there are very long time lags of unknown size between important events. This is one of the main reasons why LSTM outperforms alternative RNNs and Hidden Markov Models and other sequence learning methods in numerous applications. For example, LSTM achieved the best known results in unsegmented connected handwriting recognition, and in 2009 won the ICDAR handwriting competition. LSTM networks have also been used for automatic speech recognition, and were a major component of a network that in 2013 achieved a record 17.7% phoneme error rate on the classic TIMIT natural speech dataset.