Based on the meteorological data and satellite imageries of Lake Taihu and surrounding areas from 2005 to 2017, the paper built a cyanobacterial bloom index according to the information weight method, and analyzed the direct and indirect effects of cyanobacterial bloom from 5 meteorological factors (the annual average temperature (T_y), the average temperature from January to March(T_1-3), the annual precipitation(R_y), the precipitation from June to July(R_6-7), the annual high temperature days(D_Tmax) based on path analysis. The meteorological evaluation model of cyanobacterial bloom was built on this basis. The results show that the cyanobacterial bloom index of 2007 is the biggest (0.759), 2017 is the second (0.709), 2009 is the lowest (0.113). The cyanobacterial bloom index is basically consistent with the actual situation. T_y and T_1-3 from direct path coefficient is positive, the rest is negative, it showed that T_y and T_1-3 have positive effect on the occurrence and development of cyanobacteria bloom, however, the rest have the negative effect. The ordering of the absolute value of the total path coefficient is:T_y > T_1-3 > R_y > R_6-7 > D_Tmax, this can reflect the weight of meteorological factors affecting cyanobacteria bloom. According to this model, the correlation coefficient between the cyanobacterial bloom index and the comprehensive meteorological index passed 0.01 significance test. Then we ranked the cyanobacterial bloom index and meteorological factors according to percentile method. The total classification accuracy was 84.6%, and the moderate above it come up to 90.9%. It showed that the model can reflect the relationship between the comprehensive meteorological factors and the occurrence and development of cyanobacteria bloom better, so it can be used in the quantitative meteorological evaluation of cyanobacteria bloom in Lake Taihu under the circumstances of eutrophication degree without significant improvement. The research above help to better understand the role of environmental factors, especially meteorological factors, in the formation mechanism of cyanobacterial bloom, and provide the basis for the prediction, early warning and fine prevention & control of cyanobacteria bloom in Lake Taihu.