Digital fluorescence immunoassay (DFI) based on random dispersion magnetic beads (MBs) is one of the powerful methods for ultrasensitive determination of protein biomarkers. However, in the DFI, improving the limit of detection (LOD) is challenging since the ratio of signal-to-background and the speed of manual counting beads are low. Herein, we developed a deep-learning network (ATTBeadNet) by utilizing a new hybrid attention mechanism within a UNet3+ framework for accurately and fast counting the MBs and proposed a DFI using CdS quantum dots (QDs) with narrow peak and optical stability as reported at first time. The developed ATTBeadNet was applied to counting the MBs, resulting in the F1 score (95.91%) being higher than those of other methods (ImageJ, 68.33%; computer vision-based, 92.99%; fully convolutional network, 75.00%; mask region-based convolutional neural network, 70.34%). On principle-on-proof, a sandwich MB-based DFI was proposed, in which human interleukin-6 (IL-6) was taken as a model protein biomarker, while antibody-bound streptavidin-coated MBs were used as capture MBs and antibody-HRP-tyramide-functionalized CdS QDs were used as the binding reporter. When the developed ATTBeadNet was applied to the MB-based DFI of IL-6 (20 μL), the linear range from 5 to 100 fM and an LOD of 3.1 fM were achieved, which are better than those using the ImageJ method (linear range from 30 to 100 fM and LOD of 20 fM). This work demonstrates that the integration of the deep-learning network with DFI is a promising strategy for the highly sensitive and accurate determination of protein biomarkers.