The invention provides a closed
source model stable integration method and device based on voting weight learning, and belongs to the field of
model integration and distribution external generalization. The method comprises the steps that prediction results of a plurality of closed source models of the same type on a preset training data sample and the weight of the training data sample are acquired, then a voting weight
distributor is trained, the input of the voting weight
distributor is a sample input feature, and the output of the voting weight
distributor is the voting weight of each closed
source model; and during actual prediction, performing weighted summation on the prediction result of the to-be-predicted sample by using the closed
source model by using the voting weight output by the voting weight distributor so as to obtain a final prediction result of the to-be-predicted sample after integration. According to the method, under the condition that a plurality of closed source models are given, the prediction performance of the uncertainty
test data samples can be improved by utilizing the unique advantages of all the models, and the good prediction performance of the uncertainty
test data samples is kept.