With the increasing number of netizens, the users on the Internet has doubled, and a variety of comment data can be seen everywhere. So, it is very necessary to construct an efficient emotional classification model. This study combined Word2Vec with LSTM neural network to construct a three-class emotional classification model. Firstly, Word2Vec word vector model is used to train the emotion dictionary. Then, we construct word vectors for the current training set data by using emotional dictionary. Then, this study used the main parameters that affecting the accuracy of LSTM neural network model to train the model. The experiment found that when the data are not normalized, using the weight of He is initialized, the learning rate is 0.001, the loss function is mean square error, the RMSProp optimizer is used, the training rounds are 30, and the accuracy of traditional Word2Vec + SVM method improves by about 10%. The effect of affective classification promotes obviously, which provides a new way of thinking for LSTM model's sentiment classification.