In this paper, a multi-layer feed-forward neural network is used to construct a Meier spectrogram recognition system. By analyzing the algorithmic role of recurrent neural, the backpropagation algorithm is applied to update the weights in the neural network to obtain the mapping relationship between audio input and output. Combined with the algorithmic formula of the spectrum, the short-time Fourier transform is used to analyze the audio information. By architecting a multilayer feedforward recurrent neural network, the music signals are fused and classified. The cross-entropy loss function is applied to calculate the accuracy of micro and macro averages to improve the accuracy of music signal feature recognition. The results show that the feedforward recurrent neural network has the lowest error rate in different note recognition, and the error rate for “do” is 4%.