Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet. This framework integrates group theory, density functional theory-related mechanisms, deep learning techniques, and multiobjective optimization mechanisms, embodying a methodological fusion of data analytics and system optimization. Initially, we focus on atomization energies as the primary target of our study, using the QM9 data set to demonstrate the framework's ability to predict molecular atomization energies with high accuracy and excellent exploration performance. We then further evaluate its predictive capabilities for dipole moments and forces with the QM9X data set, achieving satisfactory results. Additionally, we tested the D3-ImgNet framework on the SN2 reaction data set to demonstrate its ability to precisely predict the minimum energy paths of SN2 chemical reactions, showcasing its portability and adaptability in chemical reaction modeling. Finally, visualizations of the electronic density generated by the framework faithfully replicate the physical phenomenon of electron density transfer. We believe that this framework has the potential to accelerate property predictions and high-throughput screening of functional materials.