The correlations between compressive strength of UHPC and its mixture composition exhibit pronounced nonlinearity, presenting a challenge for analysis through conventional statistical approaches. In this study, an automatic machine learning (AutoML) technology was proposed to predict compressive strength of UHPC, and shapley additiveex planations (SHAP) was introduced to explain the AutoML model. The integration of AutoML and SHAP offered synergistic benefits, facilitating the development of a precise, efficient, and comprehensively interpretable model. Results demonstrate that AutoML model is automatically built with better accuracy and robustness than the base model. SHAP provides a global explanation, a single sample explanation, and a feature dependence explanation of characterization factors, which explains mechanism of the effect of each characterization factor on compressive strength. SHAP contributes to the understanding of mechanism of UHPC compressive strength development and the importance of characteristic factors, and can provide assistance in the design and application of UHPC.