Optoelectronic memristors are suitable candidates for hardware implementation of optical synapses in spiking neural networks (SNNs), thanks to their electrical and optical characteristics. To study the feasibility of memristor-based optical synapses in SNNs, a behavior model for optoelectronic memristors is proposed in this paper, including electrical programming modeling and photocurrent read modeling. Based on the model, the behavior of a molecular ferroelectric (MF)/semiconductor interfacial memristor is simulated. This paper also proposes an optical synaptic circuit for trace-based spike-timing-dependent plasticity (STDP) learning rule. The electrical characteristics of the memristor are explored and exploited to emulate the trace in the pairwise nearest-neighbor STDP, while the optical characteristics are utilized for non-destructive readout and weight calculation. Synaptic-level simulation results show a 99.96% correlation coefficient (CC) and a 1.91% relative root mean square error (RRMSE) in the weight approximate computation. Extending the simulation to the network level, the optoelectronic memristor-based unsupervised STDP learning system can achieve a 92.07± 0.64% accuracy on the MNIST benchmark.