With the development of e-commerce, a large number of reviews of goods have been produced. According to short text features of commodity reviews, emotion classification based on sentiment dictionary needs a lot of emotion database resources, and machine learning method needs complex artificial design features and feature extraction process. This study adopts the short and long term memory network (Long Short Term Memory, LSTM) text classification algorithm sentiment analysis. Firstly, the text word vector is introduced into LSTM network by using Word2Vec and word segmentation technology, and finally the classification model is obtained by Dropout algorithm. Experiments show that:in deep learning based sentiment analysis of commodity reviews, the unique characteristics of short-term memory network have sound results on commodity reviews sentiment classification, the accuracy of classification is more than 99%.