The posterior distribution of traditional dynamic topic model requires complex reasoning process, and a small change in model assume will require re-deduction, meanwhile with high time cost, which restricts the variability and generality of the model. A dynamic topic model based on variational autoencoder fusing with dynamic factor graph for inference is proposed in order to improve the performance of dynamic topic model. The model makes a reparameterization trick to evidence lower bound to generate a lower estimator, and converts the hidden parameters to a group of auxiliary parameters, which makes new parameters not depend on variational parameters; standard stochastic gradient descent method can be available to variational objective function directly. At the same time, integrating the dynamic factor graph on modeling the state space model weakens the probabilistic of the model, simplifies the optimization process, and makes effective inference. The experimental results show that this model guarantees the accuracy, and the simplified model reduces the time cost effectively， which will provide more possibilities for dynamic topic model to be applied to complex time scenarios effectively.