The work aims to propose a fluorescence image classification and counting method based on support vector machine (SVM) to achieve the classification and counting of bright spots in high-throughput dPCR gene chip fluorescence image. Firstly, image preprocessing such as denoising and contrast enhancement was performed on the fluorescent image, and bright spot region was extracted annotated on the preprocessed fluorescent image to remove redundant information of background and dark points. A histogram of orientation gradient (HOG) was used to extract discriminative features, and the bright spot features of all samples were combined to obtain the HOG feature vector. A linear SVM classifier was created based on the obtained HOG feature vectors. The trained SVM classifier was used to classify and count the bright spots of the fluorescent image. Compared with traditional algorithms, the proposed algorithm had higher classification and recognition accuracy, with an average accuracy rate of more than 98%, which could well achieve the classification and counting of bright spots in fluorescent images. With limited small sample annotation data, the algorithm in this paper has good classification performance, can effectively identify bright spots in fluorescent images, and has certain reference value for other fluorescent image classification studies.