Various neural networks, including a single layer neural network (SLNN), a deep neural network (DNN) with multilayers, and a convolution neural network (CNN) have been developed and investigated to predict multiple molecular properties simultaneously. The data set of this work contains similar to 134 kilo molecules and their 15 properties (including rotational constant A, B, and C, dipole moment, isotropic polarizability, energy of HOMO, energy of LUMO, HOMO LUMO gap energy, electronic spatial extent, zero point vibrational energy, internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, free energy at 298.15 K, and heat capacity at 298.15 K) at the hybrid density functional theory (DFT) level from the QM9 database. Coulomb matrix (CM) converted from the database representing every molecule uniquely and its eigenvalue are respectively used as the input of machine learning. The accuracies of predictions have been compared among SLNN, DNN and CNN by analyzing their mean absolute errors (MAEs). Using eigenvalues as input, both SLNN and DNN can give higher accuracy for the prediction of specific energy properties (U-0, U, H, and G). For the prediction of all 15 molecular properties at a time, DNN with a 3-layers network exhibits the best results using the full CM as input. The number of layers in DNN play a key role in the prediction of multiple molecular properties simultaneously. This work may provide possibility and guidance for the selection of different neural networks and input data forms for prediction and validation of multiple parameters according to different needs.