Abstract: An increment learning algorithm based on Bayesian classifier integration is proposed to overcome the shortcomings, overloaded matching and limited classifying precision of the increment learning algorithm based on decision-making tree on a neural network. The increment classifier of simple Bayesian and integrated increment learning algorithm are combined. The SBC (simple Bayesian classifiers) is trained by random property and the increment samples are classified automatically by the tag. The results are optimized by GA (genetic algorithm). The efficiency of the increment learning algorithm based on Bayesian classifier integration has been confirmed by experimentation.