Abstract: In network computing model, trust is the major driving force for interaction and collaboration among distributed entities. How to accurately quantify and evaluate trust is the research focus. A trust forecasting model is proposed based on entity context and time stamp. Trust grade space with 8 granularity is established. The multi-dimensional measurement standard is introduced to more scientific and accurate measure of interaction satisfaction degree. The direct trust calculation method with time self-decay is proposed, which overcomes the deficiency of existing models that do not adapt to dynamic changes environment. The recommendation trust is divided to direct and indirect recommendation trust. A similarity factor of entity scoring activities is introduced to caculate direct recommendation trust. The path self-decay factor is given to calculate indirect recommendation trust. A Distributed Storage Tree (DST) mechanism is proposed, which can improve stability and scalability of model. Simulation results show that compared to the exisitng trust model, the model can more effectively and accurately provide decision-making basis, and effectively inhibit malicious entities.