Traditional Hyper-Spectral Image （HSI） spatio-spectral fusion algorithms usually use static spectral dictionary， in which the dictionary learning and the image fusion are two separate processes， thereby giving poor performance when processing noisy spatio-spectral fusion tasks. To address this problem， a noisy HSI spatio-spectral fusion algorithm based on Dynamic Dictionary Learning （DDL） was proposed， which adopted an iterative strategy that updates dictionary atoms dynamically during the fusion process， thereby collaborating to complete the spatio-spectral fusion and noise removal tasks. Firstly， a coarse denoising was performed on the input HSI and the denoising result was utilized to initialize the spectral dictionary. Secondly， the sparse representation technique was employed to fuse the two input images with the above initialized dictionary， resulting an intermediate fusion image. Thirdly， the intermediate fusion image was fed back to the dictionary learning module to update the dictionary atoms continuously， thereby forming a dynamic spectral dictionary. Finally， by iterating the above process， the final output image was obtained. Simulation results on three remote sensing HSI datasets show that the proposed algorithm can remove noise effectively while improving spatial resolution of the images. At the same time， experimental results on real noisy image bands indicate that the proposed algorithm can improve visual quality of the fused images effectively. On Cuprite Mine dataset， the Peak Signal-to-Noise Ratio （PSNR） of the proposed algorithm is increased by 32.48% and 10.72% respectively compared to those of Generalized Tensor Nuclear Norm （GTNN） method and AL-NSSR method — the method of denoising first and then fusion， with Gaussian noise variance of 0.15 and amplification factor of 8.