Recent deep learning approaches for optoelectronic property prediction primarily extract a series of predefined atom and bond features, such as the formal charge on the atom, from the 2D graph of the molecule and construct the molecular representation via the message passing mechanism. Currently, the state-of-the-art (SOTA) accuracy has been attained in multiple optoelectronic property prediction tasks. However, this 2D graph convolution method disregards 3D information and restricts its applicability to tasks involving 3D information, such as predicting the transfer integral(TI) of the film system based on the geometry of bimolecules. In the prediction of charge transport properties, Bhat et al. constructed a transfer integral database using a crystal structure library and predicted the transfer integral in a crystal environment based on the 3D graph convolution molecular representation. Owing to the dearth of experimental data and certain force field parameters in the molecular dynamic (MD) simulations of conjugated systems, no structural database of optoelectronic films has been reported thus far. For film systems, most existing studies predict the transfer integral of a single system based on the Coulomb matrix. In principle, deep learning can yield a molecular representation comparable to that obtained through domain expert feature engineering by pre-training on relevant large-scale (usually over 3M) 3D positions or low-precision labels within the field. This paradigm has been demonstrated to outperform previous methods in data-rich scenarios such as large language models, small molecule drug property prediction, and general force fields of inorganic materials. To achieve accurate and efficient virtual characterization of functional materials considering both optoelectronic and transport properties, we introduce OCNet to bridge the gap in achieving general and low data-requirement molecular representations of these properties. Firstly, we construct a large-scale conjugated molecular database containing 12M GFN2-xTB optimized structures and sTDA-xTB 40 level optoelectronic properties, together with a conjugated bimolecular database with 9M bimolecular conformations collected from 100K molecular dynamic(MD) simulations of organic films and transfer integrals with GFN1-xTB 41 accuracy. Based on these two large-scale databases, our OCNet realizes a general conjugated molecular representation and a bimolecular representation using the SE(3) Transformer architecture. Additionally, for specific downstream property prediction tasks, we support domain experts in fusing domain knowledge features with molecular representations to enhance the interpretability of OCNet and further improve its performance on scarce datasets. The accuracy of our OCNet on various computationally and experimentally labeled properties is remarkably enhanced compared to the previously reported models, attesting to the superiority of our general molecular representation. Moreover, we also establish a bimolecular transfer integral dataset containing 1.8M data points computed via density functional theory (DFT) based on 45K molecular films. The accuracy performance of our OCNet on this dataset can be exploited for subsequent quantitative prediction of film transport properties, marking a significant advancement in film transfer integral prediction from single-system modeling to general model prediction. Finally, to make the OCNet more accessible to the broader research community, we provide both the OCNet code, pre-training database and corresponding molecular and bimolecular representation online. We also provide a user-friendly web application to predict downstream properties in this work. With the free and user-friendly accessibility of OCNet, we anticipate that the general molecular representation of OCNet will serve as an effective tool for the virtual characterization of the optoelectronic and transport properties of organic optoelectronic materials, expediting the development of optoelectronic devices and demonstrating the potential for application in other functional material research scenarios related to organic conjugated molecules.