In view of the problem that it is difficult to guarantee the real-time optimization of process parameters due to the mutual coupling between processes, the large amount of process data and high processing delay in the process industrial production process, an adaptive real-time optimization model of process parameters based on end-edge-cloud collaboration and MIRF_WPSO is proposed. Firstly, an end-edge-cloud collaborative real-time optimization architecture for process parameters of multi-source heterogeneous processes is built based on edge computing technology. Then, a process parameter optimization algorithm based on mutual information random forest and adaptive inertia weighted particle swarm(MIRF_WPSO) is constructed, which is deployed at the edge to realize real-time optimization of process parameters, while a self-aware update mechanism is deployed at the cloud to realize an efficient automated closed-loop network of algorithm training-updating-recall. Finally, an experimental platform is built, and the experimental results show that the “end-edge-cloud” collaborative mode effectively relieves the computational pressure on the cloud, and enables real-time and efficient self-optimized regulation of process parameters. The average standard deviation of quality index is reduced from 1.86% to 1.25%, and the optimization speed is increased by 11.4%, providing new ideas for the further development of intelligent production processes in process industries.