Abstract: The data mining method was used to deeply analyze Microcystis aeruginosa experimental simulation data collected from literatures. The results demonstrated that principal component analysis, served as a non-parametric method of classification, could be used to identify the important variables. In addition, the primary factors which influenced the growth of Microcystis aeruginosa were initial pH (pH₀), alga density (N₀) and total phosphorus (TP₀). The growth of Microcystis aeruginosa might be inhibited by reducing the value of N₀, pH₀ or TP₀. This indicated that the data mining method could make qualitative analysis for experimental simulation data of Microcystis aeruginosa.