Foreground masking and incomplete sky coverage complicate cosmic microwave background (CMB) polarization analyses by inducing mode coupling and imperfect E/B separation, with particularly strong impact on searches for primordial $B$ modes. We present SkyReconNet-P, a convolutional neural network for inpainting CMB polarization maps that extends the SkyReconNet framework to jointly reconstruct the polarization $(Q,U)$ maps from partial-sky observations. The method combines regional processing with a hybrid design, utilizing standard convolution and dilated convolution to do a multiscale feature integration. We evaluate performance at both the map and power spectrum level using two masking scenarios: a generated random mask and the Planck 2018 common polarization inpainting mask. For both masking scenarios, SkyReconNet-P reproduces the large-scale morphology of the target maps. In power-spectrum space, we find that the reconstructed $E$ mode spectrum closely tracks the target at low multipoles, while small biases emerge at higher $\ell$. For $B$ mode, the raw reconstructed spectra exhibit a larger multipole-dependent bias, which we mitigate using a simulation-based linear calibration. We show that the calibrated $B$-mode spectrum preserves more information by comparing it with spectrum estimation using pseudo-$C_{\ell }$. Finally, we demonstrate cosmological parameter inference from calibrated reconstructed spectra by fitting $(r,A_{\mathrm{lens}})$ with a Gaussian bandpower likelihood, recovering posteriors consistent with injected parameters across three test ensembles down to $r\sim {10}^{-3}$. These results support inpainting as a complementary route to cut-sky approaches when downstream pipelines can greatly benefit from statistically well-characterized, gap-filled polarization maps.

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• Cosmic microwave background
• Cosmological parameters
• Machine learning