The neural network model coupled with memristors has been extensively researched due to its ability to more accurately represent the complex dynamic characteristics of the biological nervous system. Currently, the mathematical models of memristors used to couple neural networks mainly focus on primary function, absolute value function, hyperbolic tangent function, etc. To further enrich the memristor-coupled neural networks, a new compound exponential local active memristor is proposed, taking into account the particle motion in certain hybrid semiconductors. And then use this memristor as a coupling synapse in the Hopfield neural network. Using the basic dynamic analysis method, the system’s dynamic behaviors under different parameters and the coexistence of multiple bifurcation modes under different initial values are studied. In addition, the influence of frequency change of external stimulation current on the system is also studied. The experimental results show that the internal parameters of memristor synapses regulate the system, and the system has a rich dynamic behavior, including symmetric attractor coexistence, asymmetric attractor coexistence, large-scale chaos as shown in abst.1(a) and (b), bursting oscillation, etc. Finally, the hardware of the system is implemented by the STM32 microcontroller, and the experimental results verify the realization of the system.