Abstract: In order to improve the scalability and fine-grained control ability of queries, a geographic text data keyword query based on fine-grained constraint optimization is proposed. A query standard based on fine-grained constraint optimization was formulated, aiming to find a set of objects with minimum cost subterms for Euclidean space distance constraints, and improve fine-grained control ability of queries. The algorithm provided multiple cost functions and distance functions, which can ensure multiple keyword and spatial position input queries. Furthermore, precise algorithms and provable approximate algorithms were proposed. The effectiveness of the proposed algorithm was verified through experiments on real datasets.