To ensure the acquisition of high-quality molecular and bimolecular representations with low data requirements, we pre-train the SE(3) transformer models using a self-constructed, large-scale database containing optoelectronic and electronic coupling properties. For specific downstream tasks, OCNet integrates domain-specific features with general molecular representations, enhancing its expressive power and interpretability. On QM optoelectronic datasets, experimental optoelectronic datasets, and transport-related transfer integral datasets, OCNet significantly outperforms reported state-of-the-art models in prediction accuracy. Additionally, we construct the first large-scale bimolecular transfer integral database, comprising 1.8 million DFT-accuracy data points derived from 55,000 films. This advancement demonstrates OCNet's capability to achieve quantitative predictions on the dataset, laying a foundation for developing generalizable models for transfer integrals in thin-film systems. Consequently, OCNet provides a unified framework for the virtual characterization of both optoelectronic and transport properties in functional materials, facilitating the establishment of structure-property-function relationships in optoelectronic devices. Furthermore, OCNet's molecular and bimolecular representations have great potential to be transferred to other areas related to conjugated molecular materials, such as photocatalysis and dyes.