An FBG spectral demodulation method based on deep learning was studied. The Convolutional Neural Networks(CNN) model was used to deal with the nonlinear sequence model of the overlapping spectrum, and the central wavelength of the overlapping spectrum was predicted and identified through a one-dimensional convolutional neural network. And a parallel structure of the overlapping spectrum data automatic acquisition experimental system was built to verify the high-precision demodulation of the center wavelength of the overlapping spectrum. The experiment analyzes the effects of training samples and epoch times on training time, testing time, and demodulation accuracy, and tests the computational demodulation time of the model after training. The demodulation accuracy and test time were compared with other demodulation algorithms. At the same time, the demodulation model algorithm and the peak finding algorithm at the highest point were used to compare the center wavelength value and analyze the error for the same set of spectral data. The experimental results show that the root means square error of the demodulation model is 0.082 58 pm, and the demodulation calculation time is 30.886 ms, which is used Intel(R) Core (TM) i7-8550U CPU. The research results show that the convolutional neural network model is reasonable for the accuracy of the central wavelength demodulation results of the overlapping spectrum. Compared with other algorithms, the demodulation algorithm in this article has advantages in demodulation accuracy and time. The model size is less than 400 kB, and the required computing power is small. It can be deployed in small embedded devices. It has good application prospects in large-scale airborne sensor networks and structural health monitoring.