Abstract: The existing graph network-based remote sensing image and text retrieval model has such problems as enormous parameters, low model timeliness, and large storage space requirements, etc. A lightweight remote sensing image and text retrieval approach based on combinatorial optimization is proposed. For model architecture, the parameters of simplified image and text retrieval model of lightweight convolutional module are designed based on a cross-stage fusion. For numerical quantification, the graph network mixed precision training and quantitative inference strategies are designed to increase the inference speed of the model. The experimental results on several remote sensing retrieval datasets show that, under the condition that accuracy is unaffected, the proposed method can reduce the total parameter quantity, floating point operations by over 60% compared with the typical method.