The social trust assessment can spur extensive applications such as social recommendations, shopping, financial investment strategies, etc, but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to static network topology or simplified dynamic networks, toward the social trust relationship prediction. In contrast, in this paper, we explore the social trust by taking into account the time-varying online social networks whereas the social trust relationship may vary over time. The DTrust, a dynamic graph neural network-based solution, will be proposed for accurate social trust prediction. In particular, DTrust is composed of a static aggregation unit and a dynamic unit, respectively responsible for capturing both the spatial dependence features and temporal dependence features. In the former unit, we stack multiple NNConv layers derived from the edge-conditioned convolution network for capturing the spatial dependence features correlated to the network topology and the observed social relationships. In the latter unit, a gated recurrent unit (GRU) is employed for learning the evolution law of social interaction and social trust relationships. Based on the extracted spatial and temporal features, we then employ a fully connected neural network for learning, able to predict the social trust relationships for both current and future time slots. Extensive experimental results exhibit that our DTrust can outperform the benchmark counterparts on two real-world datasets.