The invention discloses a multi-behavior migration recommendation method based on deep learning, and the method comprises the steps: firstly obtaining and processing a plurality of implicit feedback data sets of a user; constructing a base network Gb and a plurality of behavior networks G (k), and learning low-dimensional embedded representations of users and article nodes in each network by usinga network representation learning method; then, based on different influence of multiple implicit behavior feedbacks of the user on user preference modeling, using an attention mechanism for automatically learning the weight of each behavior, and acquiring fused low-dimensional embedded representation of the user and the object finally, naturally splicing and sending low-dimensional embedding vectors of the user and the articles and to a full-connection embedding layer, adopting and feeding back a preference learning method based on a deep neural network to a feedforward neural network witha hidden layer, wherein the preference of the user for articles is learned on an output layer. The method can better capture the preference of the user and realize personalized recommendation, and has the advantages of high recommendation accuracy, strong generalization ability, easiness in realization and the like.