In this paper, we extend LSTM to tree structures, in which we learn memory cells that can reflect the history memories of multiple child cells and hence multiple descendant cells. Compared with previous recursive neural networks (<cite>Socher et al., 2013</cite>; 2012) , S-LSTM has the potentials of avoiding gradient vanishing and hence may model long-distance interaction over trees.