In view of the sparse user check-in data, underutilization of review text information, and low accuracy of point-of-interest recommendation in location-based social networks, this study proposes a point-of-interest recommendation model based on review texts and the convolutional neural network (CNN), or an RT-CNN point-of-interest recommendation model for short. To start with, the Gaussian function and adjacent geographical location weighting are used to fill in the missing location information in the matrix decomposition model and thereby predict the user’s potential interest in unchecked locations. Then, review text information is processed by the convolutional neural network to mine potential features and ultimately to extract the user’s emotional tendencies in depth. The Softmax logic regression function is utilized to obtain the probabilities of the review text related to the potential features of a user and a point-of-interest location, and potential feature vectors of the user and the location are extracted by solving the objective function. Finally, the check-in behavior, geographical location influence, user emotion tendencies, user potential features, and potential features of point-of-interest locations are integrated to recommend points-of-interest. Experiments are carried out on two real check-in datasets, namely NYC and LA, on the public website Foursquare. The results show that compared with other state-of-the-art point-of-interest recommendation models, the RT-CNN model improves the accuracy rate and the recall rate and has better recommendation performance.