The strength of high-performance concrete demonstrates a intricate relationship with its components. Traditional black box models lack interpretability. As a result, they fail to reveal the actual dependency between concrete strength and its components. Using a large sample dataset of high-performance concrete, Machine Learning Models are constructed and optimized with the Hyperopt algorithm. The models are integrated with SHAP explanation and feature dependency algorithms. A visual representation of the nonlinear relationship between the strength of high-strength concrete and its components is calculated. Additionally, the interaction and coupling effects between multiple components are examined. The results indicate: Hyperopt-Extreme Gradient Boosting Model is a Trusted Machine Learning Model with high accuracy and robustness. Among the eight components(factors), age, cement, water and slag play a key controlling role in compressive strength, and their relationship with compressive strength satisfies exponential function, power function, Gaussian function, and exponential function. At the same time, there is a significant interactive coupling effect between the four key factors. The research results can provide theoretical reference for the composite design of high-strength concrete and key factor control.