Abstract: This paper proposed a multi-task attention mechanism-based no-reference quality assessment algorithm for screen content images (MTA-SCI). The MTA-SCI first used a self-attention mechanism to extract global features from screen content images, enhancing the representation of overall image information. It then applied an integrated local attention mechanism to extract local features, allowing the focus to be on attention-grabbing details within the image. Finally, a dual-channel feature mapping module predicted the quality score of the screen content image. On the SCID and SIQAD datasets, MTA-SCI achieves Spearman's rank-order correlation coefficients (SROCC) of 0.9602 and 0.9233, and Pearson linear correlation coefficients (PLCC) of 0.9609 and 0.9294, respectively. The experimental results show that the MTA-SCI achieves high accuracy in predicting screen content image quality.