A recognition and tracking network for dense neutrophil cells in the zebrafish tail was designed to address the problems that previous algorithms have low accuracy in recognizing dense cells and wrongly connected spatial trajectories. In this study, the enhanced mask region-based convolutional neural network (Mask R-CNN) was paired with the upgraded three-dimensional DeepSort to identify and track dense neutrophil cells. First, using self-built optical projection tomography (OPT) for image acquisition. Then, in the Mask R-training CNN’s module, we enhanced the Huber mask loss, adjusted the neural network parameters, and increased the gray-level dynamic range in the detection module to optimize the edge detection performance and achieve accurate cell recognition. Finally, the cell trajectory is reconstructed using DeepSort and the improved frame-by-frame correlation notion. Experimental results indicate that this method improves network training efficiency by ~50%, and cell segmentation accuracy reached 98.99% and 97.83% in XZ/YZ plane, respectively, which are significantly higher than the unimproved Mask R-CNN, U-Net, morphology, and watershed segmentation algorithms. Moreover, 75 visual zebrafish neutrophil trajectories were reconstructed. The proposed network can better recognize, segment, and reconstruct the pathway of highly overlapping cells and expand two-dimensional positioning into three-dimensional space than conventional networks. It serves as a guide for identifying and characterizing dense microorganisms and a useful model for cell stress response in pathogenic research.