In this Letter, we propose an optical delay-weight spiking neural network (SNN) architecture constructed by cascaded frequency and intensity-switched vertical-cavity surface emitting lasers (VCSELs). The synaptic delay plasticity of frequency-switched VCSELs is deeply studied by numerical analysis and simulations. The principal factors related to the delay manipulation are investigated with the tunable spiking delay up to 60 ns. Moreover, a two-layer spiking neural network based on the delay-weight supervised learning algorithm is applied to a spiking sequence pattern training task and then a classification task of the Iris dataset. The proposed optical SNN provides a compact and cost-efficient solution for delay weighted computing architecture without considerations of extra programmable optical delay lines.