The invention provides an intelligent recommendation method for building a color separation
prism optical
system, and belongs to the technical field of intelligent recommendation, and the method comprises the steps: obtaining dominant and implicit
granularity features of an optical demand, and building an optical
feature matrix after correlation mining; establishing an optical feature priority dynamic
attenuation function, hierarchically splitting matrix features through a hierarchical
encoder, and mapping the matrix features to a component
semantic space in combination with an optical component
knowledge graph; inputting a mapping result into a matching model built by an optical component heterogeneous graph neural network, and obtaining a component recommendation combination by taking light splitting precision, component compatibility and working condition adaptability as core optimization targets; and performing parameter correction and optimal value calculation on each combination, and screening a final recommendation combination to complete
system establishment. According to the method, through
deep excavation of explicit and implicit features, dynamic priority adjustment and multi-constraint component matching, the building precision, stability and adaptability of the color separation
prism optical
system are improved, meanwhile, the building efficiency is remarkably improved, and personalized requirements under complex scenes are met.