To alleviate the security concerns caused by the openness of the edge computing network and meet the time-sensitive requirements of the edge devices’ collaborative tasks, an effective trust evaluation mechanism is needed urgently to resist multi-attacks from various malicious devices. In this work, a light- weight trust mechanism based on multi-source feedback is proposed for edge computing. First, we design a light-weight data-processing algorithm executed in edge brokers and edge devices, which could reduce the data transmission pressure in communication networks effectively and work efficiently in large-scale edge networks. Then, a comprehensive evaluation method is designed for edge brokers based on the Dempster Shafer theory and multi-source feedback mechanism, which makes our mechanism more reliable and pluralistic when resisting various multi-attacks at the same time. At last, we originally develop a neural network in the centralized cloud to update edge brokers’ hyper- parameters and weights of the key factors by auditing trust evaluation results uploaded from the edge network according to deep Q-learning algorithm, which are usually weighted manually and subjectively in traditional schemes. The experimental results show the proposed trust mechanism outperforms existing methods in reliability and calculation efficiency when resisting various malicious attacks.