To solve the problems of low efficiency and high labor intensity in manual interpretation of FISH (Fluorescence In Situ Hybridization) fluorescence images, an improved YOLOv5 algorithm that integrates spatial image enhancement is proposed for intelligent cell detection in FISH fluorescence images. On the basis of the original YOLOv5 neural network model, the algorithm added a spatial image enhancement module, and the optimal enhancement coefficient of this module was selected. This module expanded the contrast adaptation range of the model to fluorescence images, and improved the feature extraction ability and cell detection accuracy of the model. The experimental results show that the mAP (Mean Average Precision) of the improved YOLOv5 model is 0.983, which achieves better training performance and convergence speed than the original model. Furthermore, the improved YOLOv5 model achieves a cell recognition rate of 91.65%, which is 9.19% higher than that of the original YOLOv5 model. Embedding the intelligent cell detection algorithm into the self-developed fluorescence image intelligent detection software, combined with fluorescence point detection algorithm, it can give effective interpretation results.