Restaurant recommendation can leverage check-ins, time, location, restaurant attributes, and user demographics to dig user's dining preference, and recommend a list of restaurants for each user. In order to fuse these data information more effectively, this study proposes a restaurant recommendation model with multiple information fusion. Firstly, this model constructs a three-dimensional tensor by using check-ins and time context, and digs some users' similar relation matrices and restaurants' similar relation matrices from additional data information. Secondly, these relation matrices and tensor are decomposed simultaneously. Then, Bayesian personalized ranking optimization criterion method (BPR Opt) and gradient descent algorithm are adopted to solve the model parameters. Finally, the proposed model generates a corresponding restaurant candidate list for target user at different time by calculating predicted tensor. A comprehensive experimental study is conducted on two real-world datasets. The experimental results not only validate the efficacy of the proposed model, which outperforms the current restaurant recommendation model and effectively alleviates influence of the data sparsity on recommendation performance, but also evaluate the efficiency of the proposed model, which has acceptable running time.