  The spectral energy distribution (SED) sequence for type Ia supernovae (SN
Ia) is modeled by an artificial neural network. The SN Ia luminosity is
characterized as a function of phase, wavelength, a color parameter and a
decline rate parameter. After training and testing the neural network, the SED
sequence could give both the spectrum with wavelength range from 3000\AA~to
8000\AA~ and the light curve with phase from 20 days before to 50 days after
the maximum luminosity for the supernovae with different colors and decline
rates. Therefore, we call this the Artificial Neural Network Spectral Light
Curve Template (ANNSLCT) model. We retrain the Joint Light-curve Analysis (JLA)
supernova sample by using the ANNSLCT model and obtain the parameters for each
supernova to make a constraint on the cosmological $\Lambda$CDM model. We find
that the best fitting values of these parameters are almost the same as those
from the JLA sample trained with the Spectral Adaptive Lightcurve Template 2
(SALT2) model. So we believe that the ANNSLCT model could be used to analyze a
large number of SN Ia multi-color light curves measured in the current and
future observational projects.
