Identification and classification of microalgae are basis and premise in the study of physiological and biochemical characteristics for microalgae.Microalgae cells mainly consist of five kinds of biological molecules,including proteins,carbonhydrates,lipids,nucleic acids and pigments.These five kinds of biological molecules contents with different ratio in microalgae cells can be utilized to identify microalgae species as a supplement method.This paper investigated the application of Raman microspectroscopy technology in the field of rapid identification on different algae species such as aschlorella sp.and chlamydomonas sp..Cultivated in the same conditions of culture medium,illumination duration and intensity,these two kinds of species of microalgae cells were immobilized by using agar,and then the samples were placed under 514.5 nm Raman laser to collect Raman spectra of different growth periods of different species.An approach to remove fluorescence background in Raman spectra called Rolling Circle Filter(RCF) algorithm was adopted to remove the fluorescent background,and then some preprocessing methods were used to offset the baseline and smooth method of Savitzky-Golay was tried to make the spectra curves of total 80 samples smoother.Then 50 samples were randomly extracted from 80 samples for modeling,and the remaining 30 samples for independent validation.This paper adopted different pretreatment methods,and used the partial least squares(PLS) to establish model between the spectral data and the microalgae species,then compared the effects of different pretreatment methods.The results showed that with Raman microspectroscopy technology,the pretreatment method of max-peak ratio standardization was a more effective identification approach which utilizes the different content ratios of pigments of different microalgae species.This method could efficiently eliminate the influence on Raman signal due to different growth stages of microalgae and decomposition of pigments contents of microalgae in vivo.Compared with other traditional classification methods,this method had significant advantages like simpler procedure and shorter testing time,and it can also avoid some subjective measurement errors caused by unskilled operations.If the threshold was set to ±0.5,the prediction accuracy can reach 100%,and when the threshold was ±0.2,the prediction accuracy reached 86.67%,which proves the proposed new method can be a good approach to identify different algae varieties.