It is very important to use the differences in blood components between species to identify species in biomedicine, medical health, customs, criminal investigation, food safety, wildlife protection and so on. However, the current research is carried out on population cells, ignoring the heterogeneity of single cells. Therefore, it is very urgent to develop a single-cell-based blood fluorescence spectral classification method. A single-cell blood classification method is proposed based on fluorescence optical tweezers and machine learning. The optical tweezers are used to achieve single-cell capture, and the single-cell fluorescence spectrum data is obtained through the fluorescence spectrum detection system. The accurate classification is realized based on the machine learning method. First, a fluorescent optical tweezers system was designed and built to realize single-cell capture, fluorescence imaging and spectral detection were obtained. Then, the whole blood solutions of horses, pigs, dogs and chickens were prepared, and using 440 nm laser light as the fluorescence excitation light source, 100 pieces of fluorescence spectrum data for each of 4 species, including horse, pig, dog and chicken, totalling 400 pieces of fluorescence spectrum data were obtained, and the preprocessing of background removal, smoothing and normalization was carried out to eliminate instrument noise and environmental interference in the signal. Subsequently, a classification model of the random forest was established, and the relationship between the number of trees in the model and the prediction accuracy was analyzed when the number of extracted features k=20, and it was found that when the decision tree was m=500, the classification accuracy tended to be stable, and at the same time obtaining a high classification accuracy and operating efficiency. Further, 30% of the sample data was set as the test set and the rest as the training set. The relationship between different wavelengths and feature importance was calculated, 10 classification accuracy rates were obtained, and the average as the model classification accuracy rate was taken. The final average accuracy rate of the test set reaches 93.1%, and the variance is 0.31%. Finally, the confusion matrix was calculated, and the models prediction accuracy was evaluated. Chickens had the highest classification accuracy, and horses had the lowest accuracy. The analysis showed that the important contributions to the classification were porphyrins, heme and flavin adenine dinucleotide. In conclusion, the study shows that the combination of fluorescent optical tweezers and machine learning methods can achieve blood classification at the single-cell level, and the high classification accuracy validates the feasibility and efficiency of the optical tweezers-based single-cell fluorescence spectroscopy detection method. At the same time, this method can meet the modeling needs without too many samples and can avoid problems such as low fluorescence self-absorption intensity caused by low concentration. It has the advantages of fast and accurate classification and has very important potential application value.