The rapid development of artificial intelligence technology has brought profound changes to the industrial field, and industrial large-scale models, as an important carrier of the new generation of artificial intelligence technology, play a key role in promoting the transformation of industrial intelligence. In response to the problems of uneven data quality, insufficient model generalization ability, and limited computing resources in the practical application of industrial large-scale models, this paper explores the optimization path from four dimensions: data level, model architecture, computational optimization, and deployment application. By analyzing the characteristics of industrial scenarios, domain knowledge based data augmentation methods, lightweight model pruning strategies, distributed training acceleration schemes, and edge intelligence deployment frameworks have been proposed, providing new ideas for improving the performance and application effectiveness of industrial large-scale models.