This paper presents a novel online learning visual servo controller integrating the FCMAC with proportion controller for the control of position of manipulator end-effector. Since the FCMAC has good learning capability and fast learning speed, and can save much computer memory space by fuzzy processing of input space division and memory unit activation, it is used to develop an adaptive control law by learning the relationship between the image feature errors and manipulator input, and the aim of online learning of the FCMAC is to minimize the output of proportion controller. Furthermore, the FCMAC has no need for models of robot manipulator and image feature extraction, so that the capability of proposed controller for tasks under uncertain environment can be improved. Finally, the proposed controller is proved to be effective by the experiment, and compared with BP neural network.