With the development of GPS positioning technology and mobile Internet, various location-based services (LBS) applications have accumulated a large amount of spatio-textual data with location and text markup. These data are widely used in location selection decision-making scenarios such as marketing and urban planning. The goal of spatio-textual location selection is to mine the optimal locations from a given candidate set to build new facilities to influence the largest number of spatio-textual objects, such as people or vehicles, where the closer the spatial location and the more similar the text, the greater the influence. However, existing solutions not only fail to consider prevalent peer competition in real life but also ignore user evaluation factors for facilities. To make more reasonable location selection decisions in a peer competition environment combined with user ratings, this study proposes a more rational spatio-textual location selection problem, CoSTUR. To solve the limitation in traditional models where objects can only be influenced by a single facility, a threshold that makes a trade-off between the certainty and quantity of facility influence on objects is introduced, which also models the real-world situation in which multiple facilities could simultaneously influence a specific user. Based on the classical competitive equalization model, quantification of competition among facilities with different ratings is achieved. To reduce the high computational cost for large volumes of data, a novel spatio-textual index structure, TaR-tree, is constructed and two pruning strategies based on influence range are designed with a combination of thresholds to achieve two branch-and-bound solutions for spatial connectivity and range queries. Experimental results on real and synthetic datasets demonstrate that the computational efficiency can be improved by nearly one order of magnitude compared to baseline algorithms, verifying the effectiveness of the proposed method.