In the process of multi-focus cell image sequence acquisition, the fluidity of the medium causes the same cell to change position in different image frames, and obvious morphological differences exist between the defocused and focused cells. Therefore, the cell image sequence cannot meet the requirements of the existing multi-focus image fusion algorithm for ensuring spatial consistency of image content. To this end, this paper proposes a multi-focus cell image fusion method based on target recognition. After locking all cells in the image sequence through the improved YOLOv10 target recognition model, target tracking is performed to correlate multiple frames of cells and their motion trajectories are confirmed. After evaluating the clarity of the associated cells, the cells with the highest clarity are selected for image segmentation, extraction, and fusion to obtain the final full-focus clear image. The proposed method and seven multi-focus image fusion algorithms are applied to multi-focus cell image fusion and the results are compared and analyzed. The peak signal-to-noise ratio of the proposed method increases by 3.9993 dB on average, and the structural similarity index is above 0.9741. The fused image retains the entire content, accurate details, and high definition, realizing high-quality fusion of multi-focus cell microscopic images.