The invention provides a classifier integration method, which comprises the following steps that: firstly, the weight of a training sample is initialized; secondly, the classifier training is carried out to the training sample, and a plurality of the optimal sub-classifiers are selected out by adopting the classifier performance evaluation norm which is described by the error seriousness and the generalization ability; thirdly, the optimal sub-classifiers are combined. The classifier performance evaluation norm adopted by the invention can precisely select out the sub-classifiers with good performance, the better of the sub-classifier performance is, the less quantity of the sub-classifiers required by obtaining the classifiers which have the same performance in a combining way is, namely the frequency and the time of the training cycle are less. Besides, the invention also adjusts and combines the classifiers through the feedback, thereby further improving the performance of the classifier.