The sparsity has always been a primary challenge for recommendation system, and information fusion recommendation can alleviate this problem by exploiting user preference through their comments, ratings, and trust information, so as to generate corresponding recommendations for target users. Full learning of user and item information is the key to build a successful recommendation system. Different users have different preferences for various items, and users’ interest preferences and social circle are changeable dynamically. A recommendation method combining deep learning and information fusion is proposed to solve the problem of sparsity. Particularly, a new deep learning model named information fusion recommendation model combining attention CNN and GNN (ACGIF for short), is constructed. First, attention mechanism is added to the CNN to process the comment information and learn the personalized representation of users and items from the comment information. It learns the comment representation based on comment coding, and learns the user/item representation in the comment through user/item coding. It adds personalized attention mechanism to filter comments with different levels of importance. Then, the rating and trust information are processed through the GNN. For each user, the diffusion process begins with the initial embedding, combining the relevant features and the free user potential vectors that capture the potential behavioral preferences. A layered influence propagation structure is designed to simulate how the user’s potential embedding evolves as the social diffusion process continues. Finally, the preference vector of the user for the item obtained from the first two parts is weighted and fused to obtain the preference vector of the final user for the item. The MAE and RMSE of the recommended results are employed as the experimenalevaluation indicators on four public data sets. The experimental results show that the proposed model has better recommendation effect and running time compared with the existing seven typical recommendation models.