Aiming at the assembly scheduling problem of optical and mechanical modules for large laser devices, a scheduling priority rule acquisition method based on artificial neural networks (ANNs) is proposed. In the offline phase, this method optimizes the scheduling data through genetic algorithms, extracts task comparison trajectories and feature data from the optimization solution, and uses ANNs to learn the task priority comparison model. In the online phase, a closed-loop decision scheduling mode is constructed based on this model to achieve rapid response and accurate decision-making in dynamic uncertain production environments. Data experiments and practical application cases verify the effectiveness of this method. With the increase of the number of optical-mechanical modules, the advantages of ANN scheduling algorithm become more obvious. When the optimization results of ANN scheduling algorithm and GA algorithm are less than 6%, the computational efficiency of the former is more than 400 times that of the latter.