Abstract: Traffic problems not only affect people’s travel, but also bring environmental pollution and safety problems. Accurate traffic flow prediction is the key to build an intelligent transportation system to prevent and alleviate traffic problems. Most of the current methods do not take into account the dynamic spatial-temporal correlation, periodicity, linear and nonlinear characteristics of traffic flow. In this paper, an information enhanced transmission spatial-temporal graph neural network is proposed, which mainly contains a multi-feature attention module, an information enhanced transmission module, a temporal attention module, and a linear-nonlinear fusion module. The multi-feature attention module captures the intrinsic connection between multiple traffic features and takes into account the periodicity of traffic flow. The information enhanced transmission module makes full use of the information in the traffic network, enhancing the information transmission capability of the traffic network and mining the complex dynamic spatial dependencies. The temporal attention module adaptively extracts the dependencies between different time intervals. Furthermore, the linear-nonlinear fusion module considers both linear and nonlinear data features. A large number of comparative experiments are conducted on real-world datasets, and the experimental results demonstrate that the method proposed in this paper has more obvious advantages in terms of traffic flow prediction performance compared with the state-of-the-art baselines.