In view of the dynamic changes in the size of computing tasks generated by end users and the low-latency and low-energy consumption requirements of services in industrial IoT scenarios,a D2D-assisted industrial IoT resource allocation model based on user willingness is proposed.First,at the user layer,every time slot t,the probability distribution function is used to update the user's willingness to become a resource giver.At the mobile edge computing (MEC)server layer,the MEC is given a decision-making function that can make judgments on terminal upload tasks and find the appropriate solution. MEC processing;secondly,based on the K-means clustering algorithm,the tasks generated by the terminal are matched to the corresponding layer for processing;finally,in the resource allocation stage, in order to solve the problem that the Q table in Q-learning is difficult to update in real time,N-DQN is proposed algorithms,fit each other using two-layer neural networks.Simulation shows that the proposed strategy reduces system energy consumption by 10%and system delay by 12%compared with traditional methods.