The compressive strength of cement is a critical indicator for evaluating its quality and ensuring the safety and durability of engineering structures. However, traditional physical testing methods, characterized by long durations and high costs, fail to meet the demands of modern intelligent construction for rapid and economical assessment. Consequently, the development of advanced predictive models is of paramount importance. Currently, prevailing predictive models often face a “trilemma” where prediction accuracy, uncertainty quantification, and model interpretability are difficult to achieve simultaneously. The “black-box” nature of these models restricts their application in safety-critical domains. To address this challenge, this paper proposes a novel Fuzzy Probabilistic Network (FuzzyProbNet). This model transforms numerical inputs into interpretable semantic concepts through a learnable fuzzification process, extracts robust deep features using a Variational Autoencoder, and ultimately generates a complete predictive probability distribution via a Gaussian Mixture output head. Experimental results demonstrate that the proposed FuzzyProbNet outperforms baseline models across various metrics for both point and probabilistic prediction. Furthermore, visualization and analysis of the model’s internal workings validate its clear decision-making logic and inherent interpretability.