In the construction of the model decision tree, there are many parameters and the parameter combination is complex. The use of grid search and other parameter tuning methods will consume a lot of time, which will affect the improvement of the model performance. A model decision tree with multi-kernel bayesian optimization was proposed. In order to deal with the characteristics of different classified data, three Gaussian processes were used for modeling optimization. The Bayesian optimization technique was used to select the best parameter combination. The experimental results show that the proposed algorithm is better than the traditional model decision tree method in parameter optimization, and can find the global optimal parameter value in the case of a few iterations. To a certain extent, it improves the classification performance of the algorithm and saves a lot of parameter adjustment time.