In order to improve the accuracy and efficiency of ideal strain design in cereal grains, an intelligent design system based on gene and phenome clustering of strain height module was constructed, integrating high-throughput SNP data processing, ReliefF gene feature screening, GMM clustering modeling and Bayesian optimization recommender mechanism, to realize the rapid evolution of the target strains in a three-layer heterogeneous structure. Through the validation of phenotype-gene joint tensor modeling and machine learning constructs on 1280 sets of grain samples, the system achieves an R2 of 0.926, an MSE of 0.022, and a diversity score of 0.85 in plant height prediction, and the optimization module achieves a convergence rate of 96.8% within 150 rounds of iterations, and the recommendation paths have a strong convergence and exploratory ability in the latent space. The analysis concluded that the fine identification and high-dimensional clustering expression of the plant height module significantly enhanced the stability and genetic adaptability of ideal strain combinations, and promoted the modeling expression and intelligent practice of precision breeding in cereals.