No-Reference Image Quality Assessment (NR-IQA) methods has made significant progress with the rapid development of deep learning techniques. However, most NR-IQA methods usually use a single attention mechanism for feature interaction, which limits the prediction performances of these NR-IQA methods in complex scenarios. To address this, we propose a Feature Enhancement and Feature Interaction Network (FEFI-Net) for NR-IQA. The key contributions are the multi-scale attention enhancement module and the multi-attention feature interaction module. The multi-scale attention enhancement module is designed to enhance the pre-trained features extracted by Swin Transformer, thereby obtaining more comprehensive feature representations. The multi-attention feature interaction module with three attentions is designed to establish more accurate feature interaction. Comparative experiments on five widely-used IQA datasets demonstrate that our FEFI-Net surpasses some state-of-the-art NR-IQA methods in prediction accuracy.