Abstract: This disclosure provides a sequential recommendation method based on long-term and short-term interests, in which an interaction sequence between a user and products is obtained by processing a purchase sequence of the user and a question data of the user in a dataset, characteristics of the products are represented with extracted comments of the user on the products; next, a stable long-term preference of the user is learned from a historical purchase sequence of the user with a recursive neural network, and immediate interests of the user are modeled with the question data. For the stable long-term preference and dynamic immediate interests, a dependence of different users on the two characteristics is described with an Attention mechanism, so as to effectively solve a problem of an inaccurate recommendation caused by an evolution of the preference of the user, while different dependence degrees of the different users on the long-term preference and immediate interests can represented effectively.