With the widespread application of digital images in various domains, the accurate measurement of image quality has become particularly crucial. This article introduces a novel multibranch multilayer feature fusion network (MFFNet) to address the inadequate expression of multiscale and semantic features and local visual feature consideration in existing no-reference image quality assessment (NR-IQA) algorithms. MFFNet comprises a primary and a sub-branch. Through convolutional neural network (CNN) feature extraction, the main branch uses a multiscale feature enhancement (MSFE) module to capture fine-grained features at each layer, thus significantly enhancing its capability to represent local features. It subsequently merges these distinct-scale features through the multilayer feature fusion (MLFF) module to improve MFFNet performance. Recognizing human attention to the local image area during image quality evaluation, the sub-branch acquires local visual information using a classical superpixel segmentation model. Finally, the two branches are fused using an element-by-element multiplication operation. Comparative experiments are conducted using four representative datasets—CSIQ, TID2013, LIVEC, and CID2013—demonstrating that the MFFNet method outperforms most advanced techniques, thereby establishing the method’s effectiveness.