The invention discloses a nonlinear 
laser fluorescence spectrum real-time identification method, which comprises the following steps: learning a sample spectrum, testing sample spectral classification, extracting ROI in an interested region, preprocessing the spectrum, extracting the 
fluorescence spectrum characteristics by discrete 
curvelet transform, forming feature vectors, constructing i classes of support vector machines, and distinguishing the test results by classes. The invention adopts the classification method of the support vector machines, and does not depend on 
large sample training, the input vector is the low-frequency coefficient part after 
curvelet decomposition, the number of training samples is small, the number of the support vectors is greatly reduced, so the 
operation time is shortened and the method has instantaneity. The second-generation 
curvelet transform adopted by the invention is based on a new support frame, and can provide high-efficient, stable and nearly-optimal sparse representation for the curve function with 
strangeness. Compared with the traditional method, the method is more effective and has higher 
identification rate. The invention can identify the spectrum samples with 
data format and image format, and has better adaptability.