There are non-uniform distribution characteristics of brightness， noise， and contrast in low-light images， however， the existing Low-Light Image Enhancement （LLIE） algorithms fail to fully utilize these characteristics. As a result， issues such as detail loss， color distortion， and visual discontinuity may occur， affecting the visual quality of the images. To address these problems， a noise and semantic prior guided LLIE algorithm was proposed to consider characteristics of different regions in low-light images and their semantic information adaptively. Specifically， a novel Image block Classification based Global Feature Extraction network （ICGFE） was designed to extract global features， an Information Compensation based Local Feature Extraction network （ICLFE） was introduced to extract local features， and a noise prior-guided feature fusion strategy was proposed to perform adaptive enhancement operations on image regions with different characteristics. Furthermore， a new semantic prior-guided color loss function was presented to maintain consistency of instance colors. Experimental results on the public dataset LOL （LOw-Light dataset） show that the proposed algorithm improves the Peak Signal-to-Noise Ratio （PSNR） by 1.9%-89.1% and achieves good results in Structural SIMilarity （SSIM） compared to the algorithms such as Retinex and Underexposed Photo Enhancement using Deep illumination estimation （DeepUPE）. It can be seen that the proposed algorithm can enhance image regions with different characteristics adaptively and has significant advantages in perspectives such as color restoration， detail and texture reconstruction， and noise suppression.