This study proposes a new paradigm of dynamic prior modulation based on degradation kernel decoupling evaluation to address the key challenges of kernel estimation bias and non blind method prior mismatch in blind super resolution reconstruction. By establishing a decoupling evaluation mechanism for degraded kernel window width amplitude, it is revealed that the estimation error of kernel window width has a decisive impact on the generalization performance of non blind reconstruction networks. Based on this, this work innovatively constructs a two-stage optimization framework: introducing a loss function relaxation constraint strategy in the kernel estimation stage, enhancing the compatibility between the estimation kernel and non blind priors by avoiding excessive loss functions affecting the accurate estimation of kernel window width; simultaneously design a dynamic kernel prior modulation network, adopting a dual path feature collaborative optimization mechanism. The sharpening feature module extracts image sharpening prior through high-frequency gradient enhancement, while the fuzzy attenuation feature module suppresses noise interference through mean filtering and extracts fuzzy attenuation prior features with regional degradation differences. The two generate degradation modulation vectors through prior modulation layers to achieve dynamic calibration of the kernel feature space. Experimental verification shows that dynamic kernel prior modulation network improves PSNR by 1.92 dB in Set5 dataset with 2×Gaussian blur scenes and 0.61 dB in BSD100 dataset with 4×strong noise scenes, significantly better than existing optimal methods. This method effectively solves the problem of kernel prior mismatch in complex degraded scenarios, providing an innovative solution for blind super-resolution reconstruction in actual complex degraded scenarios.