The invention provides a method for automatic optimal model selection based on big data. The method comprises: step S1, classifying mining targets; step S2, using information gain to perform rapid feature selection on a whole data set; step S3, establishing a training set and a verification set; step S4, selecting an effective data mining algorithm and a parameter combination thereof; step S5, using a Bayes optimization method to select effective parameter combinations of each algorithm; step S6, selecting an optimal data mining algorithm K; step S7, using cross validation, selecting and determining a parameter value combination of the data mining algorithm K, to obtain a final model; step S8, if a result obtained by the model is relatively poor, repeating steps S2-S7, selecting the optimal model again, until a model result is satisfied; if the result is relatively satisfied, outputting the model. The method can save time consumed by automatic modeling, and modeling efficiency is improved, and the optimal algorithm can be searched rapidly from large quantity of algorithms, and parameter combinations in the optimal algorithm are selected by cross validation.