In the mobile crowd sensing (MCS) network environment, it is very important to establish an evolutionary process that can dynamically depict the trust degree of task participants. To address this issue, this paper proposes a dynamic trust evaluation model for task participants. Firstly, according to the security requirements and trust strategy of the perceived tasks, the attribute reduction algorithm (ARA) based on rough set is used to obtain the multi-attribute indexes that affect the participants' trust information. Removing the redundant attributes can avoid the lag of trust evaluation and reduce the time cost. Secondly, the grey correlation analysis method is used to solve the correlation degree between the target sequence and the comparison sequence on the trust attributes by integrating the multi-attribute decision-making method, which avoids the distortion of the trust evaluation caused by human subjective factors and improves the quality of the perceived data. Finally, a dynamic trust evaluation model for participants in complex sensing network environment is established. The simulation results show that the proposed model can not only dynamically depict the trust degree of participants in real time, but also have higher accuracy and less time cost.