Rotating machinery equipment is the key equipment in industrial production, which is of great significance to carry out efficient fault diagnosis for industrial safety production. The traditional intelligent fault diagnosis method of rotating machinery adopts the strategy of artificial feature extraction, which has the limitations of relying on expert experience knowledge, poor feature generalization and insufficient feature completeness, leading to poor precision of the fault diagnosis model, especially in the noisy environment. To solve the above problems, a multimode coupled input neural network model for rotating machinery fault diagnosis is proposed. Firstly, the raw input signal is decomposed into several sub-signals using the signal decomposition method, and the sub-signals are paired with the raw signal to form a two-dimensional matrix and input into the network, so that the network can extract important related features between the raw signal and sub-signals. In addition, the two-channel parallel convolutional neural network and long and short-term memory network are used to extract and fuse the time-space features of signals, which greatly improves the feature expression completeness of the network model and realizes the high-precision fault classification of rotating machinery. The results of the experiments show that the proposed model has higher accuracy and better adaptability to noise disturbance than traditional fault models.