Electromagnetic simulation plays a crucial role in microwave filter design, but traditional numerical methods are computationally expensive and struggle to handle the many-to-many mapping problem from design parameters to electromagnetic responses. To rapidly and accurately establish parametric models for microwave filters, this paper proposes a data-driven surrogate model based on a hybrid architecture combining conditional variational autoencoder (CVAE) and Transformer. Firstly, an S-parameter response database for a fourth-order cavity filter was constructed using electromagnetic simulation software. Subsequently, a hybrid CVAE-Transformer model was built based on this database. The model introduces a probabilistic latent space and designs an innovative composite loss function that incorporates a hierarchical decibel-domain adaptive weight mechanism, achieving a high-fidelity mapping from structural parameters to the probability distribution of S11 parameters. Experimental results show that compared to several benchmark models, the surrogate model exhibits optimal performance, with a mean squared error (MSE) as low as 0.002 324 and a correlation coefficient as high as 0.996 6. Compared to traditional electromagnetic simulation methods, the prediction speed is improved by more than three orders of magnitude. Therefore, the rapid and accurate prediction capability of the surrogate model proposed in this paper has significant theoretical guidance and practical engineering application value for the robust design, yield analysis, and optimization design of microwave filters.