IHMCAP (incremental hybrid multi-concepts acquisition) algorithm combines the p robability based symbolic learning with neural learning. The balance of learning accuracy between the symbolic and the neural parts are proportioned successfull y, and the two different levels of thought are aboard laid by adhibiting FTART ( field theory-based adaptive resonance theory) neural network. A unique incremen tal learning mechanism is employed with this algorithm, which can adjust the fo rmer structure to improve learning accuracy by learning once instead of rebuildi ng the decision tree and the neural networks when the new examples are provided. It has higher speed, and is efficient. Moreover, the noisy sensibility of the s ystem is depressed by the incremental learning mechanism, which enables IHMCAP c an be applied to the tasks that require real-time online learning.