Breast cancer is one of the most common malignant tumors, and its molecular typing can effectively guide individualized treatment. However, its clinical detection is relatively complex and time consuming. Fluorescence spectroscopy is convenient and efficient; hence, it is widely used for tumor detection. Meanwhile, studies involving molecular typing are few. In this study, a new method for detecting breast cancer via molecular typing is proposed, which is based on a principal component analysis and support vector machine (PCA-SVM) model combined with fluorescence spectroscopy. A laser-induced fluorescence detection system with a wavelength of 405 nm is constructed and the autofluorescence spectra of breast cancer tissues are measured. The results of Gaussian fitting show that the peak intensity of fluorophores in different subtypes of breast cancer are different. Through further analysis using the PCA-SVM model, the molecular subtypes of breast cancer are identified with an accuracy of 95.0%, a precision of 95.9%, and a balanced F-score of 94.9%. Moreover, the discriminant sensitivities of the four subtypes are 91%, 94%, 97%, and 100%. This scheme improves the analysis efficiency of high-dimensional spectral data and provides a new direction for the rapid identification of breast cancer via molecular typing.