Existing super-resolution methods assume a predefined degradation process from high-resolution images to low-resolution images, which is difficult to hold for real-world images with complex degradation types. For this problem, a blind super-resolution model based on degradation-aware is proposed. The model generates low-resolution images with random blur kernels and learns degenerate representations with contrasts. The model generator consists of residual groups containing multiple degradation-aware blocks. Degraded perceptual blocks use degraded representations and image features to do cross-attention to calculate spatial weight maps. In addition, the model collects layer-level features from the output of residual groups and calculates inter-layer attention to reuse layer-level features. This enables the model to pay more attention to high-frequency details, and the model feature extraction capability is further improved. The effectiveness of each module is verified by ablation experiments. On multiple international public test sets, the average PSNR with a magnification of 4 is increased by 1.45 dB, and the SSIM is increased by 0.058. Experimental results show that the model achieves significant performance on blind super-resolution tasks with good visual results.