Hot subdwarf stars are important tracers of stellar structure and evolution, while their binary systems provide key constraints on their formation channels. Nevertheless, the number of confirmed hot subdwarf binaries is still limited. We aim to identify hot subdwarf binaries efficiently and reliably in large candidate samples by combining heterogeneous observational information. We developed a two-stage classification framework. A Bayesian neural network was first used to perform preliminary classification and uncertainty quantification from Gaia photometric and astrometric data. We then constructed a multimodal deep learning model, HsdB-FusionNet, which combines Gaia photometric and astrometric features with SDSS spectra through a cross-attention mechanism. We applied this framework to a catalog of 61 585 hot subdwarf candidates. The resulting candidates were further examined through spectral energy distribution fitting, and atmospheric parameters were derived for high-quality systems. We identified 1369 hot subdwarf binary candidates from the parent sample. Among them, 1057 objects were strongly supported as binaries by spectral energy distribution fitting. Atmospheric parameters were obtained for 579 high-quality binary systems. The interpretability analysis further indicates that the model decisions are consistent with known physical priors. This work provides a substantially enlarged sample of hot subdwarf binary candidates and demonstrates the potential of multimodal deep learning for binary identification. The released catalog offers a useful basis for future studies on the formation and evolution of hot subdwarf binaries.