Abstract: Aiming at the ability of self-organization and generalization of bionic autonomous learning system,this paper constructs a dynamic fuzzy operant conditioning probabilistic automaton(FOCPA) bionic autonomous learning system based on Skinner operant conditioning(OC) theory and fuzzy clustering algorithm.The dynamic FOCPA learning system not only has bionic self-learning and self-organizing ability,but also can improve the learning speed and precision of learning system. Under the learning environment where only weak feedback information can be obtained,the FOCPA learning system firstly adopts online clustering algorithm to flexibly divide the input space to ensure that the number of mapping rules is the most economical.And then the learning system takes orientation value as evaluation signal and adopts the designed OC learning algorithm to autonomously learn the optimal mapping online from input states to output operant action,and a Gaussian noise term is added for optimizing the mapping result in real time.Moreover,by using the evaluating ability of information entropy, the self-learning and self-organizing ability is verified.The convergence of OC learning algorithm is proved from theory,and the further experiments on posture balancing control and velocity control of two-wheeled flexible upright robot prove the validity of dynamic FOCPA learning system.