The assessment for cell physiology and growth phases of microalgae plays important roles in ecological and environmental fields since it can be used to forecast water eutrophication level worldwidely. Herein, growth phases and environmental conditions of microalgae were assessed by combining resonance Raman mapping spectroscopy with multivariate analysis methods. And, primary Raman characteristic peaks of microalgae were mined with two-dimensional synchronous spectra. Thereafter, algal growth phases and environmental conditions of microalgae were preliminary classified with different tendencies of characteristic Raman peaks by unsupervised principal component analysis (PCA) and support vector machine (SVM) methods. Our results demonstrated that resonance Raman mapping spectroscopy with PCA and SVM classification models can be used to assess algal growth phases and preliminary predict environmental conditions with characteristic Raman spectra of microalgae in water bodies. (C) 2018 Elsevier B.V. All rights reserved.