Finding reliable partners is the key to supply chain management. However, the symmetrical evaluation of enterprise trust is complex, so the decision-makers must understand its quantitative and qualitative characteristics in order to realize a reasonable evaluation. Based on the analysis of the causes and influencing factors of supply chain trust, this paper constructed four primary indexes and 16 secondary indexes to define enterprise trust, and used analytic network process (ANP) to evaluate and rank the indicators. Then, the paper constructed a supply chain directed weighted trust evolution network model based on complex network theory, integrated trust into the network with edge weights, and put forward the merit index of comprehensive node degree, weight, and efficiency to study the supply chain network evolution. The simulation results show that the node degree distribution in the trust evolution network conforms to the power-law distribution rule, and the trust evolution model of the complex network has obvious scale-free characteristics, which effectively avoid the situation that the node influence is too high due to the excessive strength of a single index. At the same time, it can quickly evaluate the node influence of the directed weighted complex network, and provide certain practical value for the node trust prediction of the supply chain network.

We study the structure of supply chain trust networks (SCTNs) by analyzing the evolution of the networks. An SCTN here comprises enterprises in a fully competitive market connected through the preferential attachment mechanism. A Markov chain analysis is used to understand how various factors affect the structure of the SCTNs. The evolution of the SCTNs is also analyzed to identify the asymmetric conditions required for the degree distribution of the SCTNs to obey the power law distribution. The simulation results show that, when the degree of willingness to initiate a trust relationship and the attractiveness index of the supply chain networks meet certain criteria, the underlying network is of a scale-free nature.