Abstract: The monitoring parameters of coal mine equipment are time-series data, and the time-series characteristics have great influence on health assessment. The traditional mechanical equipment health assessment has the problems of incomplete extraction of signal spatiotemporal characteristics, high dependence on human experience, and difficult assessment of early condition change of equipment. In order to solve these problems. A two-dimensional array long short-term memory denoising convolutional autoencoder (2D-LSTMDCAE) model is constructed, and a health index (HI) construction and condition assessment method of coal mine rotating machinery based on 2D-LSTMDCAE is proposed. The one-dimensional vibration data is converted into a two-dimensional array. The two-dimensional convolution network model is used to fully learn the information contained in the original data, so the learning capability of the model on data characteristics is enhanced. The samples are input into convolution and long short-term memory (LSTM) units in parallel to obtain complete signal spatiotemporal characteristics. The unsupervised learning denoising convolutional autoencoder (DCAE) model is constructed for sample reconstruction. The similarity between the original sample and the reconstructed sample is calculated by Bray-Curtis distance to obtain the HI. It solves the problem that it is difficult to obtain the condition tag during the operation of the equipment, and improves the adaptability of the model in strong background noise. The characteristic learning capability of the 2D-LSTMDCAE model is verified by using XJTU-SY bearing data set. The two indexes of correlation and monotonicity are adopted to evaluate the condition assessment method based on HI. The test results show the following points. The two-dimensional input sample construction method and the HI construction method of learning the time series characteristics of data are more sensitive to the performance degradation of bearings. The 2D-LSTMDCAE model can detect the early failure of the equipment earlier. On the test bearing, the HI and RMS constructed by the 2D-LSTMDCAE model are about 7 min earlier than that of the LSTMDCAE and DCAE models. Compared with the HI and RMS constructed by the LSTMDCAE and DCAE models, the HI constructed by the 2D-LSTMDCAE model has higher correlation and monotonicity, and it can better reflect the degradation of bearings. The health assessment experiment is carried out by using the accelerated degradation experimental data of the reducer. On the test reducer, compared with RMS, it can detect early failure 8 min in advance by using the HI constructed by the 2D-LSTMDCAE model. The correlation is improved by 0.007, and the monotonicity is improved by 0.211, which can better reflect the degradation situation of the reducer.