Breast cancer is a very dangerous disease for women worldwide, its prevalence is increasing year by year, and it is the main cause of death among women worldwide. In the case of large samples, the clinical diagnosis of breast cancer is limited by the relative shortage of high-quality medical resources, the diagnosis cycle is long, and the detection cost is high. Therefore, efficient, accurate and cost-effective breast cancer diagnosis methods have broad application prospects and are urgently needed for clinical diagnosis. Fluorescence spectroscopy is a method that can characterize the combined physical and chemical changes in cells and can be used to characterize normal and cancerous cells. Machine learning is good at mining useful information from a large amount of data and is an effective classification and prediction method. In the past, machine learning mostly used databases containing some biochemical information to train models, which easily led to information loss. The fluorescence spectrum is the superimposed spectrum of multiple substances in cells, and the use of fluorescence spectrum characteristic peaks to diagnose breast cancer has the problem of quantitative uncertainty.Therefore, this paper proposes a diagnostic method combining machine learning with fluorescence spectra of breast cancer samples. The fluorescence spectrum data of normal and cancerous breast tissue (pathological diagnosis has been made) was collected as a data set, and K-nearest Neighbor (KNN), support vector machine (SVM), Random Forest (RF) three algorithms to classify the fluorescence spectrum of normal and cancerous breast tissue. The discriminant results show that compared with the SVM algorithm, the KNN and RF algorithms have higher accuracy, stronger ability to balance recall and precision, and better classification ability for breast cancer fluorescence spectra. The results of the F1-score function are all above 95%, which is more conducive to the diagnosis of breast cancer. Furthermore, the classification ability of the Weighted K-nearest Neighbor (WKNN) algorithm for normal and cancerous breast tissue fluorescence spectra were discussed. Compared with the KNN algorithm, WKNN has a small improvement in the classification evaluation results and has better anti-noise and adaptability, and the algorithm is simple. In conclusion, the breast cancer diagnosis method based on machine learning and fluorescence spectroscopy proposed in this paper has high accuracy, high speed and high-cost performance. It is the future development direction of breast cancer diagnosis methods and has important clinical application value.