In order to explore rapid real-time algae detection methods, in the present study experiments were carried out to use fluorescence spectral imaging technology combined with a pattern recognition method for identification research of different types of algae. The fluorescence effect of algae samples is obvious during the detection. The fluorescence spectral imaging system was adopted to collect spectral images of 40 algal samples. Through image denoising, binarization processing and making sure the effective pixels, the spectral curves of each sample were drawn according to the spectral cube .The spectra in the 400～720 nm wavelength range were obtained. Then, two pattern recognition methods, i.e. hierarchical cluster analysis and principal component analysis, were used to process the spectral data. The hierarchical cluster analysis results showed that the Euclidean distance method and average weighted method were used to calculate the cluster distance between samples, and the samples could be correctly classified at a level of the distance L=2.452 or above, with an accuracy of 100%. The principal component analysis results showed that first-order derivative, second-order derivative, multiplicative scatter correction, standard normal variate and other pretreatments were carried out on raw spectral data, then principal component analysis was conducted, among which the identification effect after the second-order derivative pretreatment was shown to be the most effective, and eight types of algae samples were independently distributed in the principal component eigenspace. It was thus shown that it was feasible to use fluorescence spectral imaging technology combined with cluster analysis and principal component analysis for algae identification. The method had the characteristics of being easy to operate, fast and nondestructive.