In order to solve the problem of single feature dimension and low accuracy in monitoring gearbox wear condition from vibration and oil features alone,a gearbox wear condition monitoring algorithm based on oil-vibration multi-dimensional features and particle swarm optimization algorithm-long and short term memory neural network (PSO-LSTM) was proposed.The ferrography images were preprocessed to extract the features of wear particle concentration and the number of wear particles.The vibration signal was denoised by wavelet threshold,and the time-domain features were extracted.The 14-dimensional characteristics of oil-vibration were obtained as the input of LSTM model.Particle swarm optimization algorithm was used to optimize the parameters of LSTM model.The experimental results show that the recognition accuracy of the PSO-LSTM model using the 14-dimensional features of oil-vibration is better than that of the PSO-LSTM model using the vibration and oil characteristics alone.The recognition accuracy of the PSO-LSTM model is better than that of the unoptimized LSTM model in all aspects.