In order to solve the Lambert model is difficult to calculate the indoor visible light channel noise and error problems,a neural network algorithm is proposed to realize the indoor visible light channel model.Aiming at the problems of large amount of fingerprint database data,difficult collection and many training parameters,which lead to slow iteration speed,the generative adversarial network (GAN) is proposed to generate simulation data set and merge the original sparse fingerprint database to generate the number of fingerprint database meeting the training requirements.A one-dimensional convolutional neural network (CNN) is used to extract data features,reduce training parameters and improve iteration speed.The sparse fingerprint database is collected in the indoor environment of 5 m×5 m×3 m,and the back propagation neural network (BPNN) and one-dimensional CNN indoor visible light channel model are respectively used for comparison.The simulation results show that the average absolute error of GAN is 0.04,and the data volume is increased by 300%.Under the same fingerprint database,the error of BPNN channel model is 3.81,and the convergence is realized after 500 iterations.However,the error of CNN channel model is 0.79,and the iteration converges are 100 times.The GAN fingerprint database merged CNN visible light channel model proposed in this paper has the advantages of high precision,small error,fast speed and strong generalization,which provides a new research scheme for indoor visible light channel model.