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Associative memory is a widespread self-learning method in biological livings, which enables the nervous system to remember the relationship between two concurrent events. The significance of rebuilding associative memory at a behavior level is not only to reveal a way of designing a brain-like self-learning neuromorphic system but also to explore a method of comprehending the learning mechanism of a nervous system. In this paper, an associative memory learning at a behavior level is realized that successfully associates concurrent visual and auditory information together (pronunciation and image of digits). The task is achieved by associating the large-scale artificial neural networks (ANNs) together instead of relating multiple analog signals. In this way, the information carried and preprocessed by these ANNs can be associated. A neuron has been designed, named signal intensity encoding neurons (SIENs), to encode the output data of the ANNs into the magnitude and frequency of the analog spiking signals. Then, the spiking signals are correlated together with an associative neural network, implemented with a three-dimensional (3-D) memristor array. Furthermore, the selector devices in the traditional memristor cells limiting the design area have been avoided by our novel memristor weight updating scheme. With the novel SIENs, the 3-D memristive synapse, and the proposed memristor weight updating scheme, the simulation results demonstrate that our proposed associative memory learning method and the corresponding circuit implementations successfully associate the pronunciation and image of digits together, which mimics a human-like associative memory learning behavior.