Strength prediction method of high performance concrete based on stacking model fusion was proposed to address the issues of large deviations and low efficiency of traditional empirical formulas for high-performance concrete strength prediction. Firstly1 030 sets of high-performance concrete compressive strength test data were preprocessed through data cleaning and normalization to eliminate abnormal data and the dimensional influence among data. Secondlybased on extreme gradient boosting (XGBoost)category boostingmulti-layer perceptronand random forest (RF) algorithmshyperparameter optimizationmodel training and evaluation were conductedand the overall effect of the four base learners on strength prediction were compared and analyzed using coefficient of determination R2root mean square error and mean absolute error. Based on thisa Stacking ensemble learning model was constructedwhich fuses multiple machine learning algorithms for concrete strength prediction. Finallythe model was validated using 103 sets of new dataand interpretable analysis was performed. The results show that compared to other combinations of base learnersthe fusion model using XGBoost and RF significantly improves prediction accuracy and performanceand has good generalization performance. The interpretable analysis shows that the most important input feature variables are age and cementindicating that the internal prediction logic of the model is more in line with engineering practice experiencehaving high rationality and reliability. The research results provide reference for further improving the accuracy of high-performance concrete strength prediction.