Chemical kinetics plays an important role in governing the thermal evolution in reactive flows problems. The possible interactions between chemical species increase drastically with the number of species considered in the system. Var-ious ways have been proposed before to simplify chemical networks with an aim to reduce the computational complexity of the chemical network. These techniques oftentimes require domain-knowledge experts to handcraftedly identify important reaction pathways and possible simplifications. Here, we propose a combination ofautoencoder and neural ordinary differential equation to model the temporal evolution of chemical kinetics in a reduced subspace. We demonstrated that our model has achieved a 10-fold speedup compared to commonly used astro-chemistry solver for a 9-species primordial network, while maintaining 1 percent accuracy across a wide range of density and temperature.