The number of escalators in China has been increasing in recent years. However, regular inspection, supervision, spot check, and other common methods are hardly to inspect the potential failure inside the escalator. An improved fault diagnosis method based on CNN-LSTM neural network is proposed in this paper. Besides, a software for escalator monitoring and fault diagnosis is designed with LabVIEW. Furthermore, an improved method to fuse the shallow and deep data features to improve the accuracy of fault diagnosis based on the CNN-LSTM neural network algorithm is proposed in this paper. The fault diagnosis method proposed is tested with the data of Case Western Reserve University. The results show that the method is efficient and effective, and the fault diagnosis accuracy is 99.4%. Moreover, the software for escalator monitoring and fault diagnosis is designed. there vibration sensors are set on the key parts of the escalator, and monitoring software is used for data acquisition, data display and data storage. After collecting a large amount of operating condition data, the of typical faults can be diagnosed.