Molecular property prediction is a hot issue in the field of material chemistry. Although the calculation method based on the first principle can clearly describe the electron distribution in the system, the calculation process is too complex, and the calculation complexity increases exponentially with the increase of atoms in the molecule. In recent years, with the deepening of related research, a variety of deep learning algorithms have emerged. The algorithms were divided into two categories based on multi-layer perceptron ( MLP) and graph neural network ( GNN) and six sub categories, and the characteristics of different algorithms were studied. The analysis shows that MLP algorithm has simple structure, limited expansibility and low correlation with the internal structure of molecules. On the contrary, GNN class algorithms integrate the message passing mechanism, and transform the interaction between molecules into the feature transfer between nodes and edges, which is superior in the evaluation indexes of all directions. At present, the molecular property prediction algorithms based on deep learning is developing from MLP algorithm to GNN algorithm. Based on the above analysis, this paper puts forward the development direction of molecular property prediction algorithm based on deep learning in data set, anisotropic feature transfer, guiding the practical application in material science and Life Science in the future. [ABSTRACT FROM AUTHOR]