While peer-to-peer networks bring convenient services to users, their own characteristics such as openness, anonymity, dynamics, and autonomy also bring about some credibility issues, such as free-riding by malicious nodes and false evaluations, malicious services and other untrustworthy behaviors. At the same time, there is a lack of management mechanism in peer-to-peer network applications, and resource sharing between users relies heavily on mutual trust between nodes, which cannot ensure the authenticity of resources. To address this problem, the author constructed a P2P network to simulate node transaction behavior and untrustworthy behaviors such as free riding and malicious services, and obtained node dynamic data. The obtained data set is analyzed through the AHP analytic hierarchy process to obtain the subjective weight of the node attributes, and the principal component analysis method is used to analyze the data set to obtain the objective weight. The comprehensive weight of node attributes is obtained by weighting with game theory ideas. In this article, we mainly describe the acquisition of node attribute data through simulation experiments, and propose a subjective and objective weighting method based on AHP and PCA, which effectively reduces the impact of user evaluation subjectivity on weights.
