The invention relates to an information recommendation method based on a convolutional neural network and a joint attention mechanism, which is used for effectively utilizing potential semantic information of text and overcoming inherent defects of a feature extraction method of traditional machine learning. According to the method, feature vectors of the evaluation text processed by a CNN deep neural network is processed by a layer of attention mechanism, so that the attention weight of key points of interest in the evaluation text is increased. The vector sets of users and projects and thescore of the previous attention mechanism respectively use a layer of attention mechanism to acquire attention mechanism weight vectors of the users and the projects respectively. Point multiplication is carried out on the attention mechanism weight vectors and vector sets of the users and the projects respectively to obtain final representation, the users, the projects and the evaluation text are combined to obtain the final representation, and score prediction is made. Compared with traditional recommendation technology, the method has the advantages that recommendation can be performed more effectively, the recommendation quality is improved, and the interpretability of recommendation is enhanced.