Abstract: Uncertainty in the microstructure of random porous materials propagates to the uncertainty of macroscopic responses through fundamental physical laws. This process requires a comprehensive description of the spatial uncertainty at the microscopic level, which involves the introduction of a high-dimensional joint probability density function. Additionally, the uncertainty need to be coupled with nonlinear multi-scale propagation, which creates significant challenges for the uncertainty quantification of macroscopic material performance. To address this issue, this study proposes a data-physics dual-driven uncertainty analysis framework integrating variational autoencoders and probability density evolution theory, and achieves the propagation of microstructural uncertainty to homogenized stress-strain response uncertainty under uniaxial compression. Initially, a data-driven variational autoencoder (VAE) is used to map the complex, high-dimensional microstructure to a low-dimensional latent space. In this space, the spatial uncertainty is represented by the probability distribution of the latent variables. This method effectively reduces the dimensionality of high-dimensional random variables while preserving the key probabilistic characteristics of the microstructure. Afterward, sampling is performed in the probability space of the latent variables, and the samples are then decoded back to the original pixel space to reconstruct the microstructure. This enables the generation of new microstructure samples that reflect the uncertainty in the original material system. Moreover, based on a physics-driven theory of probability density evolution,