Efficient and accurate computation of the single‐scattering properties of black carbon (BC) aerosols is fundamental in various fields, including remote sensing and climate simulations. In this study, we developed a composite model of fractal aggregates of BC encapsulated with hygroscopic aerosols to represent the ambient BC. We used the invariant imbedding T‐matrix method to compute the optical properties of fully and partially encapsulated BC aerosols. In this new model, the traditional assumption of unoverlapped surfaces in the super‐position T‐matrix method is unnecessary. After extensive simulations, we established a database of single‐scattering properties, including the extinction efficiency, the single‐scattering albedo, the asymmetry factor and six phase matrix elements. Moreover, we obtained deep neural networks (DNNs) from this database using a deep learning method. These DNN models provide a universal interface for predicting the optical properties of ambient BC aerosols. Specifically, through a modified architecture of the DNN, we trained two models based on the database to predict three integrated optical properties (extinction efficiency, single‐scattering albedo, and asymmetry factor) and six phase matrix elements. We performed statistical assessments based on the true values in the database and the predicted values from the DNNs, demonstrating that the DNNs accurately predicted all single‐scattering properties. Therefore, the developed DNN models can be conveniently implemented in aerosol optical parameterization for remote sensing studies and atmospheric models. Plain Language Summary: Black carbon (BC) aerosol plays a significant role in the atmosphere, influencing climate through its interactions with radiation, such as scattering and absorbing. The accurate determination of the single‐scattering properties of BC, including the extinction efficiency, the single‐scattering albedo, the asymmetry factor and six phase matrix elements, is crucial for understanding its radiation effects. In this study, we developed a comprehensive model for BC particles that incorporates complex morphological characteristics and the mixing of BC with other hygroscopic aerosols. We established a database of single‐scattering properties for these models using the invariant imbedding T‐matrix method. However, the large storage space required by this database (over 20 GB) makes it impractical for widespread applications. Therefore, we employed a deep learning method to compress the storage consumption. Two deep neural networks (DNNs) were trained using the database, enabling the prediction of all single‐scattering properties. Remarkably, the performance of the DNNs was excellent, with a coefficient of determination greater than 0.99 for all single‐scattering properties, as determined through statistical assessment. These DNN models hold great potential for various atmospheric radiation and remote sensing studies in the future. Key Points: A flexible model of partially and fully encapsulated fractal black carbon particles was developedA database of single‐scattering properties of encapsulated fractal black carbon particles was constructedTwo deep neural networks were obtained, offering a flexible approach to calculating the optical properties of black carbon aerosols [ABSTRACT FROM AUTHOR]