The invention discloses a commodity recommendation method combining an attention network and user emotion. The method comprises the steps: 1) extracting the score and comment data of a user for a commodity, carrying out the preprocessing, and constructing a sample set T; 2) continuously training to obtain an attribute matrix W of a corresponding field by adopting an unsupervised learning model andutilizing text data commented in the T; 3) constructing a neural network structure C based on attention; constructing a user preference vector U and a commodity feature vector I by using a W-based memory network and a recurrent neural network as bases and using the predicted emotion score as a weight, calculating a prediction value of a missing score by using the U and the I, and calculating attribute vectors of a current user and a commodity for final recommendation explanation; and 4) performing descending sort according to the predicted scores, recommending the first N commodities to the user, and providing explanation of attribute levels for a recommendation result according to the attribute matrix and the attribute vectors, thereby solving the problems of lack of explanation, difficulty in processing large-scale data and the like of a traditional score prediction recommendation method.