Abstract: To address the need for microstructure characterization and metallographic analysis of high-temperature alloy materials, this paper proposes an intelligent microstructure generation method that combines ultrasonic evaluation with a diffusion-based generative model. Based on geometric parameters obtained from ultrasonic testing (such as grain size, roundness, and aspect ratio), this study reconstructs the microstructure of specific alloys through the dual input of data accumulation and ultrasonic evaluation. Experimental results show that the generated virtual metallographs closely match the real ones in terms of morphology and geometric features, with grain geometry errors controlled within 3%. This method provides a new direction for the intelligent generation and precise characterization of material microstructures.