A remote sensing image super-resolution reconstruction algorithm based on conditional prior enhancement and diffusion model is proposed to address the problems of blurry reconstruction effect of small targets in remote sensing images and loss of high-frequency details during the reconstruction process. Firstly, a shallow feature enhancement module that integrates multi branch standard convolution, dilated convolution, and coordinate attention is used to enhance the perception ability of small targets. Secondly, by stacking residual dense blocks, more representational features can be extracted while maintaining training stability; Subsequently, a multi-scale depth separable convolution module was designed to extract multi-scale prior information and prevent the loss of high-frequency details; Finally, the combination of the above modules is input as prior information into the diffusion model, guiding it to iteratively refine and generate high-resolution images. The experimental results on the publicly available remote sensing image dataset RSCNN7 and NWPU-RESISC45 show that good performance is achieved when the scale factor is ×2, ×4, and ×8. Among them, on the RSCNN7,when the scale factor is ×4, compared with methods with different network architectures, the proposed model significantly reduces the PI and FID, compared to SOTA algorithm based on diffusion model, it reduces 1.43 and 20.56, respectively. In terms of subjective visual effects, it is closer to the true value compared to the comparison algorithm.