Anchoveta (Engraulis ringens) is a kind of small pelagic fish living in the Southeast Pacific Ocean. It is also an important source of fishmeal. Predicting the anchoveta biomass effectively and evaluating their relationship with environment factors could benefit companies which import the Peruvian fishmeal. Therefore, this study firstly used the grey correlation analysis to analyze the connection between the anchoveta biomass and environmental factors from 2004 to 2013. And then based on these results, we used the grey forecasting model[GM (0, N) model] to build the anchoveta biomass forecasting model. In addition, by comparing between removing a certain environmental factor and containing all the factors to the model, we evaluated the importance of environmental factors. Results showed that the model which contained all the factors(factors including Fishing ground temperature, FGT, Fishing ground temperature anomaly, FGTA, Southern Oscillation Index, SOI, the sea surface temperatureat Nino 1+2 region, Nino 1+2 and Pacific Decadal Oscillation Index, PDO) had the mean relative error of 0.197 between fitting biomass sequence and predicted biomass sequence; the coefficient correlation index between these two sequences was 0.544; the relative error of the validation data is 0.434. Comparing the models from model 2 to model 6 which removed one environmental factor, the model 4 which removed PDOI had the best result:the mean relative error of fitting biomass sequence and predicted biomass sequence was 0.187, the coefficient correlation index between these two sequences was 0.663, and the relative error of the validation data was 0.274. The results indicated that model 4 can improve the accuracy of forecasting model and could be set as the optimal model for predicting the anchoveta biomass.