The invention discloses a recommendation system click rate prediction method based on a deep neural network, and the method comprises the steps: collecting a user click behavior as a sample, extracting numerical features of the sample with a numerical value relation, and inputting the numerical features into a GBDT tree model for training, and obtaining a GBDT leaf node matrix E1; inputting a behavior sequence formed by clicking the articles by all the users in the sample into an Attention network to obtain an interest intensity matrix E2 of all the users in the sample for the articles; summing and averaging the article feature vectors of the click interaction of the user to obtain a click interaction matrix E3 corresponding to the user, splicing E1, E2 and E3, and inputting the E1, E2 andE3 into a deep neural network model with three hidden layers and one output layer to output a prediction result. According to the method, user clicking behaviors are decomposed into attribute characteristics, a GBDT tree model, an Attention network and a deep neural network model are subjected to nonlinear fitting, a recommendation system clicking rate prediction model is constructed, a prediction result is obtained through model training, and the method has the advantages of deep mining of recent interests of users, high generalization degree and high expansibility.