In the field of second language acquisition, overshadowing and blocking by cue competition effects in classical conditioning affect the learning and expression of human cognitive associations. In this work, a memristive neural network circuit based on neurobiological mechanisms is proposed, which consists of synapse module, neuron module, and control module. In particular, the designed network introduces an inhibitory interneuron to divide memristive synapses into excitatory and inhibitory memristive synapses, so as to mimic synaptic plasticity better. In addition, the proposed circuit can implement six functions of second language acquisition conditioning, including learning, overshadowing, blocking, recovery from overshadowing, recovery from blocking, and long-term effect of overshadowing over time leading to blocking. Overshadowing, which denotes that the more salient stimulus overshadows the learning of the less salient stimulus when two stimuli differ in salience, reduces the associative strength acquired by the less salient stimulus. Blocking, which indicates that pretraining on one stimulus blocks learning about a second stimulus, inhibits the associative strength acquired by a second stimulus. Finally, the correctness and effectiveness of implementing functions mentioned above are verified by the simulation results in PSPICE. Through further research, the proposed circuit is applied to bionic devices such as social robots or educational robots, which can address language and cognitive disorders via assisted learning and training.