Blind image quality assessment (BIQA) is crucial for user satisfaction and the performance of various image processing applications. Most BIQA methods directly use the pre-trained model to extract features and then perform feature fusion. However, the features extracted by pre-trained models may contain irrelevant information to BIQA. Although some methodspre-train the feature extraction network from scratch, these approaches raise computational costs and resource demands.