With the rapid advancement of artificial intelligence (AI) in industrial applications, this paper proposes a novel intelligent optimization method based on a hybrid data-model-driven approach to address traditional challenges in loader excavation trajectory optimization, including high data acquisition costs and computational complexity.

Under the background of "five aspects of education in parallel", integrating aesthetic education into engineering training is crucial for cultivating all-round engineering innovation talents. The engineering training center provides natural support for the implementation of aesthetic education.